R Notebook
Loading the Packages
library(pacman)
#p_install_gh("favstats/favstats")
p_load(tidyverse, haven,sjmisc,
forcats, weights, car,
countrycode, lavaan,
semTools, lavaan.survey,
survey, reshape2, favstats,
magrittr)Loading the URLs
afro5_url <- url("https://github.com/favstats/database_delib/raw//master/afro5.RData")
afro6_url <- url("https://github.com/favstats/database_delib/raw//master/afro6.RData")
latino2013_url <- url("https://github.com/favstats/database_delib/raw//master/latino2013.RData")
latino2015_url <- url("https://github.com/favstats/database_delib/raw//master/latino2015.RData")
wvs_raw_url <- url("https://github.com/favstats/database_delib/raw//master/wvs_raw.RData")
americas_url <- url("https://github.com/favstats/database_delib/raw//master/americas.RData")
bolivia_url <- url("https://github.com/favstats/database_delib/raw//master/bolivia.RData")
canada_url <- url("https://github.com/favstats/database_delib/raw//master/canada.RData")
asian_raw_url <- url("https://github.com/favstats/database_delib/raw//master/asian_raw.RData")
myanmar_raw_url <- url("https://github.com/favstats/database_delib/raw//master/myanmar_raw.RData")
mongolia_raw_url <- url("https://github.com/favstats/database_delib/raw//master/mongolia_raw.RData")
philip_raw_url <- url("https://github.com/favstats/database_delib/raw//master/philip_raw.RData")
taiwan_raw_url <- url("https://github.com/favstats/database_delib/raw//master/taiwan_raw.RData")
thai_raw_url <- url("https://github.com/favstats/database_delib/raw//master/thai_raw.RData")
malay_raw_url <- url("https://github.com/favstats/database_delib/raw//master/malay_raw.RData")
singapore_raw_url <- url("https://github.com/favstats/database_delib/raw//master/singapore_raw.RData")
sk_raw_url <- url("https://github.com/favstats/database_delib/raw//master/sk_raw.RData")
cambodia_raw_url <- url("https://github.com/favstats/database_delib/raw//master/cambodia_raw.RData")
ESS_raw_url <- url("https://github.com/favstats/database_delib/blob/master/ESS_raw.Rdata?raw=true")
vdems_start_url <- url("https://github.com/favstats/database_delib/raw//master/vdems_start.Rdata")
qog_url <- url("https://github.com/favstats/database_delib/raw//master/qog.Rdata")Afrobarometer Data
Afro 5
load(afro5_url)
delete_na_afro <- function(x) {
x <- Recode(x, "9 = NA;
98 = NA;
99 = NA;
-1 = NA")
return(x)
} # Funktion um die NAs im Afro Datensatz zu bestimmen
# alle Variablen
afro_5 <- afro5 %>%
rename(
educ = Q97, # Variablen umbenennen
income = Q3B,
sex = Q101,
work = Q96,
trust_gov = Q59A,
trust_parliament = Q59B,
trust_police = Q59H,
trust_courts = Q59J,
demtoday = Q46A
) %>%
mutate_at(
vars(
income, trust_gov, trust_parliament,
trust_police, trust_courts, work
),
delete_na_afro
) %>% # NAs deleten
mutate(
sex = sex - 1, # sex (0/1) codieren
work = Recode(
work, # work (0/1) codieren
"1 = 0;
2 = 1;
3 = 1"
),
age = Recode(
Q1, # missing values l?schen
"-1 = NA;
998 = NA;
999 = NA"
),
demtoday = Recode(
demtoday,
"-1 = NA;
98 = NA;
99 = NA"
),
educ = Recode(
educ,
"-1 = NA;
98 = NA;
99 = NA;
999 = NA"
),
cntry = to_label(COUNTRY_ALPHA),
year = as.numeric(format(DATEINTR, "%Y"))
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
afro_5Afro 6
load(afro6_url)
afro_6 <- afro6 %>%
rename(
educ = Q97,
income = Q4B,
sex = Q101,
work = Q95,
trust_gov = Q52A,
trust_parliament = Q52B,
trust_police = Q52H,
trust_courts = Q52J,
demtoday = Q40
) %>%
mutate_at(
vars(
income, demtoday, trust_gov,
trust_parliament, trust_police,
trust_courts, work
),
delete_na_afro
) %>% # NAs deleten
mutate(
sex = sex - 1, # sex (0/1) codieren
work = Recode(
work, # work (0/1) codieren
"1 = 0;
2 = 1;
3 = 1"
),
age = Recode(
Q1, # missing values l?schen
"-1 = NA;
998 = NA;
999 = NA"
),
educ = Recode(
educ,
"-1 = NA;
98 = NA;
99 = NA;
999 = NA"
),
demtoday = Recode(
demtoday,
"8 = NA"
),
cntry = to_label(COUNTRY_R5List),
year = as.numeric(format(DATEINTR, "%Y"))
) %>%
# mutate_at(vars(income, demtoday, educ, age,
# trust_gov, trust_parliament,
# trust_police, trust_courts),
# as.character) %>%
# mutate_at(vars(income, demtoday, educ, age,
# trust_gov, trust_parliament,
# trust_police, trust_courts),
# as.numeric) %>%
select(
cntry, year, age, sex, income,
educ, work, demtoday,
trust_gov, trust_parliament,
trust_police, trust_courts
)
afro_6Merging the Data
afro_real <- afro_5 %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
afro_real2 <- afro_6 %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
# length(unique(afro_real$cntry))
# length(unique(afro_real2$cntry))
# table(afro_real2$year)
afro <- rbind(afro_real, afro_real2) %>% as.tbl()
afroLatino Barometro
Latino 2013
load(latino2013_url)
latino_2013 <- latino2013 %>%
rename(
educ = REEDUC_1,
income = S6,
demtoday = P50TGB.A,
age = S11
) %>%
mutate(
sex = S10 - 1, # sex variable erstellen
cntry = to_label(IDENPA), # cntry variable erstellen
trust_gov = 5 - P26TGB.B,
trust_parliament = 5 - P26TGB.C,
trust_police = 5 - P28TGB.B,
trust_courts = 5 - P26TGB.E,
year = 2013,
income = 5 - income,
work = Recode(
S19.A,
"2 = 1;
3 = 1;
4 = 0;
5 = 0;
6 = 0;
7 = 0"
)
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
latino_2013Latino 2015
load(latino2015_url)
latino_2015 <- latino2015 %>%
mutate(sex = S12 - 1) %>% # sex variable erstellen
rename(
educ = REEDUC_1,
income = S4,
demtoday = P17STGBS,
age = S13
) %>%
mutate(
trust_gov = 5 - P16ST.G,
trust_parliament = 5 - P16ST.F,
trust_police = 5 - P16TGB.B,
trust_courts = 5 - P16ST.H,
work = Recode(
S21.A,
"2 = 1;
3 = 1;
4 = 0;
5 = 0;
6 = 0;
7 = 0"
),
cntry = to_label(IDENPA),
income = 5 - income,
year = 2015
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
latino_2015Merging the Data
latino_real <- latino_2013 %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
latino_real2 <- latino_2015 %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
latino <- rbind(latino_real, latino_real2) %>% as.tbl()
latinoWorld Value Survey
load(wvs_raw_url)
wvs <- wvs_raw %>%
rename(
educ = V248,
income = V239,
demtoday = V141,
age = V242,
year = V262
) %>%
mutate(
sex = V240 - 1, # sex variable erstellen
trust_gov = 5 - V115,
trust_parliament = 5 - V117,
trust_police = 5 - V113,
trust_courts = 5 - V114,
work = Recode(
V229,
"2 = 1;
3 = 1;
4 = 0;
5 = 0;
6 = 0;
7 = 0;
8 = 0"
),
cntry = to_label(V2)
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament,
trust_police, trust_courts
)Warning: closing unused connection 23 (https://github.com/favstats/database_delib/raw//master/qog.Rdata)
Warning: closing unused connection 22 (https://github.com/favstats/database_delib/raw//master/vdems_start.Rdata)wvsAmericas Barometer
Americas Main
load(americas_url)
americas_ <- americas %>%
mutate(cntry = to_label(pais)) %>%
select(
cntry, ocup4a, ed, q10new, q1, q2,
b10a, b13, b18, b21a, n3, year
) %>%
rename(
work = ocup4a,
educ = ed,
income = q10new,
sex = q1,
age = q2,
trust_courts = b10a,
trust_parliament = b13,
trust_police = b18,
trust_gov = b21a,
demtoday = n3
) %>%
mutate(
sex = sex - 1, # sex variable erstellen
work = Recode(
work,
"2 = 1;
3 = 1;
4 = 0;
5 = 0;
6 = 0;
7 = 0"
)
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
americas_Bolivia
load(bolivia_url)
bolivia_ <- bolivia %>%
mutate(cntry = to_label(pais)) %>%
select(
cntry, ocup4a, ed, q10new, q1, q2,
b10a, b13, b18, b21a, n3, year
) %>%
rename(
work = ocup4a,
educ = ed,
income = q10new,
sex = q1,
age = q2,
trust_courts = b10a,
trust_parliament = b13,
trust_police = b18,
trust_gov = b21a, # viele missing values
demtoday = n3
) %>%
mutate(
sex = sex - 1, # sex variable erstellen
work = Recode(
work,
"2 = 1;
3 = 1;
4 = 0;
5 = 0;
6 = 0;
7 = 0"
)
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
bolivia_Canada
load(canada_url)
canada_ <- canada %>%
mutate(cntry = to_label(pais)) %>%
select(
cntry, exc13, education, q10, q1, q2,
b10a, b13, b18, b21a, n3, year
) %>%
rename(
work = exc13,
educ = education,
income = q10,
sex = q1,
age = q2,
trust_courts = b10a,
trust_parliament = b13,
trust_police = b18,
trust_gov = b21a,
demtoday = n3
) %>%
mutate(
sex = sex - 1, # sex variable erstellen
work = work - 1,
income = ifelse(income == 88, NA, income)
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
canada_Merging the Data
americas <- americas_ %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
bolivia <- bolivia_ %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
canada <- canada_ %>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
americas <- rbind(americas, bolivia, canada)
americasAsian Barometer
load(asian_raw_url)
delete_na_asian <- function(x) {
x <- Recode(
x,
"-2 = NA;
7 = NA;
8 = NA;
9 = NA;
97 = NA;
98 = NA;
99 = NA;
-1 = NA"
)
return(x)
} # Funktion um die NAs im asian Datensatz zu bestimmen
asian_3 <- asian_raw %>%
rename(
educ = se5,
trust_gov = q9,
trust_parliament = q11,
trust_police = q14,
trust_courts = q8,
sex = se2,
income = se13a,
work = se9
) %>%
mutate_at(
vars(
income, trust_gov, trust_parliament,
trust_police, trust_courts, work, sex
),
delete_na_asian
) %>% # NAs deleten
mutate(
trust_gov = 5 - trust_gov,
trust_parliament = 5 - trust_parliament,
trust_police = 5 - trust_police,
trust_courts = 5 - trust_courts,
sex = sex - 1, # sex (0/1) codieren
income = Recode(
income,
"0 = NA"
),
income = 5 - income,
work = Recode(
work, # work (0/1) codieren
"2 = 0"
),
age = Recode(
se3a, # missing values l?schen
"-1 = NA"
),
demtoday = Recode(
q91,
"-1 = NA;
97 = NA;
98 = NA;
99 = NA"
),
educ = Recode(
educ,
"-1 = NA;
98 = NA;
99 = NA"
),
cntry = to_label(country),
year = as.numeric(format(ir9, "%Y"))
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
asian_3Wave 4
load(myanmar_raw_url)
needed <- function(data) {
ss <- data %>%
rename(
educ = se5,
trust_gov = q9,
trust_parliament = q11,
trust_police = q14,
trust_courts = q8,
sex = se2,
income = se13a,
work = se9
) %>%
mutate_at(
vars(
trust_gov, trust_parliament,
trust_police, trust_courts, work, sex
),
delete_na_asian
) %>% # NAs deleten
mutate(
trust_gov = 5 - trust_gov,
trust_parliament = 5 - trust_parliament,
trust_police = 5 - trust_police,
trust_courts = 5 - trust_courts,
sex = sex - 1, # sex (0/1) codieren
income = Recode(
income,
"0 = NA;
97 = NA;
98 = NA;
99 = NA;
-1 = NA"
),
work = Recode(
work, # work (0/1) codieren
"2 = 0"
),
age = Recode(
se3_2, # missing values l?schen
"-1 = NA"
),
demtoday = Recode(
q94,
"-1 = NA;
97 = NA;
98 = NA;
99 = NA"
),
educ = Recode(
educ,
"-1 = NA;
98 = NA;
99 = NA"
),
cntry = to_label(country),
year = year
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)
return(ss)
}
myanmar <- needed(myanmar_raw)
load(mongolia_raw_url)
load(philip_raw_url)
load(taiwan_raw_url)
load(thai_raw_url)
load(malay_raw_url)
load(singapore_raw_url)
load(sk_raw_url)
load(cambodia_raw_url)
mongolia <- needed(mongolia_raw)
philip <- needed(philip_raw)
thai <- needed(thai_raw)
malay <- needed(malay_raw)
singapore <- needed(singapore_raw) # Singapore hat extrem viele Missing values
sk <- needed(sk_raw)
cambodia <- needed(cambodia_raw)Sonderfall Taiwan
# Taiwan Problem: Income only has 3 categories
taiwan <- taiwan_raw %>%
rename(
educ = se5,
trust_gov = q9,
trust_parliament = q11,
trust_police = q14,
trust_courts = q8,
sex = se2,
income = se14a,
work = se9
) %>%
mutate_at(
vars(
income, trust_gov, trust_parliament,
trust_police, trust_courts, work, sex
),
delete_na_asian
) %>% # NAs deleten
mutate(
trust_gov = 5 - trust_gov,
trust_parliament = 5 - trust_parliament,
trust_police = 5 - trust_police,
trust_courts = 5 - trust_courts,
sex = sex - 1, # sex (0/1) codieren
income = Recode(
4 - income,
"8 = NA;
9 = NA"
),
work = Recode(
work, # work (0/1) codieren
"2 = 0"
),
age = Recode(
se3_2, # missing values l?schen
"-1 = NA"
),
demtoday = Recode(
q94,
"-1 = NA;
97 = NA;
98 = NA;
99 = NA"
),
educ = Recode(
educ,
"-1 = NA;
98 = NA;
99 = NA"
),
cntry = to_label(country),
year = year
) %>%
select(
cntry, year, age, sex, income, educ, work, demtoday,
trust_gov, trust_parliament, trust_police, trust_courts
)Merging Data
asian_3 %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
myanmar %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
mongolia %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
philip %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
taiwan %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
thai %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
malay %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
singapore %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
sk %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
cambodia %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
asian <- rbind(
asian_3, myanmar, cambodia, sk, singapore, malay, thai, taiwan, philip,
cambodia, mongolia
) %>% as.tbl()
asianMerging Everything
afro %<>%
mutate(survey = rep("afro", nrow(afro))) %>%
mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))
latino %<>%
mutate(survey = rep("latino", nrow(latino))) %>%
mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))
americas %<>%
mutate(survey = rep("americas", nrow(americas))) %>%
mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))Some values were not matched unambiguously: Hait攼㹤asian %<>%
mutate(survey = rep("asian", nrow(asian))) %>%
mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))
wvs %<>%
mutate(survey = rep("wvs", nrow(wvs))) %>%
mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en")) %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
ESS %<>%
mutate(survey = rep("ESS", nrow(ESS))) %>%
mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en")) %<>%
mutate_at(
vars(
income, demtoday, educ,
trust_gov, trust_parliament,
trust_police, trust_courts
),
range01
)
unique(ESS$cntry) [1] "Albania"
[2] "Belgium"
[3] "Bulgaria"
[4] "Switzerland"
[5] "Cyprus"
[6] "Czech Republic"
[7] "Germany"
[8] "Denmark"
[9] "Estonia"
[10] "Spain"
[11] "Finland"
[12] "France"
[13] "United Kingdom of Great Britain and Northern Ireland"
[14] "Hungary"
[15] "Ireland"
[16] "Israel"
[17] "Iceland"
[18] "Italy"
[19] "Lithuania"
[20] "Netherlands"
[21] "Norway"
[22] "Poland"
[23] "Portugal"
[24] "Russian Federation"
[25] "Sweden"
[26] "Slovenia"
[27] "Slovakia"
[28] "Ukraine"
[29] "Kosovo" unique(asian$cntry) [1] "Japan" "Hong Kong"
[3] "Republic of Korea" "China"
[5] "Mongolia" "Philippines"
[7] "Taiwan, Province of China" "Thailand"
[9] "Indonesia" "Singapore"
[11] "Viet Nam" "Cambodia"
[13] "Malaysia" "Myanmar" unique(wvs$cntry) [1] "Algeria" "Argentina"
[3] "Armenia" "Australia"
[5] "Azerbaijan" "Bahrain"
[7] "Belarus" "Brazil"
[9] "Colombia" "Cyprus"
[11] "Chile" "China"
[13] "Ecuador" "Egypt"
[15] "Estonia" "Georgia"
[17] "Germany" "Ghana"
[19] "Hong Kong" "India"
[21] "Iraq" "Japan"
[23] "Jordan" "Kazakhstan"
[25] "Kuwait" "Kyrgyzstan"
[27] "Lebanon" "Libya"
[29] "Malaysia" "Mexico"
[31] "Morocco" "Netherlands"
[33] "New Zealand" "Nigeria"
[35] "Pakistan" "Palestine, State of"
[37] "Peru" "Philippines"
[39] "Poland" "Qatar"
[41] "Romania" "Russian Federation"
[43] "Rwanda" "Singapore"
[45] "Slovenia" "Republic of Korea"
[47] "South Africa" "Spain"
[49] "Sweden" "Taiwan, Province of China"
[51] "Thailand" "Trinidad and Tobago"
[53] "Tunisia" "Turkey"
[55] "Ukraine" "United States of America"
[57] "Uruguay" "Uzbekistan"
[59] "Yemen" "Zimbabwe" unique(latino$cntry) [1] "Argentina" "Bolivia (Plurinational State of)"
[3] "Brazil" "Colombia"
[5] "Costa Rica" "Chile"
[7] "Ecuador" "El Salvador"
[9] "Spain" "Guatemala"
[11] "Honduras" "Mexico"
[13] "Nicaragua" "Panama"
[15] "Paraguay" "Peru"
[17] "Dominican Republic" "Uruguay"
[19] "Venezuela, Bolivarian Republic of"unique(americas$cntry) [1] "Bahamas" "Barbados"
[3] "Belize" "Brazil"
[5] "Colombia" "Costa Rica"
[7] "Dominican Republic" "Ecuador"
[9] "El Salvador" "Guatemala"
[11] "Guyana" NA
[13] "Honduras" "Jamaica"
[15] "Mexico" "Nicaragua"
[17] "Panama" "Paraguay"
[19] "Peru" "Suriname"
[21] "Trinidad and Tobago" "Uruguay"
[23] "Bolivia (Plurinational State of)" "Canada" unique(afro$cntry) [1] "Algeria" "Burundi"
[3] "Benin" "Burkina Faso"
[5] "Botswana" "Cameroon"
[7] "Côte D'Ivoire" "Cabo Verde"
[9] "Egypt" "Ghana"
[11] "Guinea" "Kenya"
[13] "Lesotho" "Liberia"
[15] "Madagascar" "Mauritius"
[17] "Mali" "Malawi"
[19] "Mozambique" "Morocco"
[21] "Namibia" "Niger"
[23] "Nigeria" "South Africa"
[25] "Senegal" "Sierra Leone"
[27] "Sudan" "Swaziland"
[29] "United Republic of Tanzania" "Togo"
[31] "Tunisia" "Uganda"
[33] "Zambia" "Zimbabwe"
[35] "Gabon" "Sao Tome and Principe" merged <- rbind(wvs, latino, afro, americas, asian, ESS)
merged %<>%
# create dummies
mutate(
wvs = ifelse(survey == "wvs", 1, 0),
afro = ifelse(survey == "afro", 1, 0),
latino = ifelse(survey == "latino", 1, 0),
americas = ifelse(survey == "americas", 1, 0),
asian = ifelse(survey == "asian", 1, 0),
ESS = ifelse(survey == "ESS", 1, 0)
) %>%
# filter bad countries
filter(cntry != "Egypt") %>% # exclude Egypt 2013
filter(cntry != "Libya") %>% # exclude Libya 2014
filter(cntry != "Mali") %>% # exclude Mali 2012
filter(cntry != "Yemen") %>% # exclude Yemen 2012
filter(cntry != "Palestine, State of") # exclude Palestine 2013
# adding weight
merged %<>%
group_by(cntry) %>%
tally() %>%
mutate(weight = 1000 / n) %>%
select(cntry, weight) %>%
left_join(merged, "cntry")
# select(cntry, year) %>%
# unique %>%
# View
mergedtable(merged$wvs)
0 1
301009 80618 table(merged$afro)
0 1
280893 100734 table(merged$latino)
0 1
338714 42913 table(merged$americas)
0 1
311125 70502 table(merged$asian)
0 1
349440 32187 table(merged$ESS)
0 1
326954 54673 SEM Index
merged2 <- merged %>%
mutate(gov_trust = trust_gov + trust_parliament +
trust_police + trust_courts) %>%
filter(!is.na(gov_trust))
merged3 <- merged %>%
mutate(gov_trust = trust_gov + trust_parliament +
trust_police + trust_courts) %>%
filter(is.na(gov_trust))
svy.df <- survey::svydesign(id= ~1,
weights= ~weight,
data= merged)
model <- '# measurement model 1
gov_trust2 =~ 1*trust_gov + trust_parliament +
trust_police + trust_courts
trust_gov ~~ trust_parliament
'
merged <- merged %>%
mutate_at(vars(trust_gov, trust_parliament,
trust_police, trust_courts), as.numeric)
# cor(na.omit(data.frame(merged$trust_gov,
# merged$trust_police,
# merged$trust_courts,
# merged$trust_parliament,
# merged$demtoday)))
lavaan_model1<-cfa(model, meanstructure = T,
data = as.data.frame(merged),
estimator= "MLM")
fit_a1<-lavaan.survey(lavaan_model1,
estimator= "MLM", survey.design=svy.df)
summary(fit_a1, standardized=TRUE,fit.measures = TRUE, rsq = T)lavaan (0.5-23.1097) converged normally after 38 iterations
Number of observations 339509
Estimator ML Robust
Minimum Function Test Statistic 4.858 2.577
Degrees of freedom 1 1
P-value (Chi-square) 0.028 0.108
Scaling correction factor 1.885
for the Satorra-Bentler correction
Model test baseline model:
Minimum Function Test Statistic 503887.369 334369.533
Degrees of freedom 6 6
P-value 0.000 0.000
User model versus baseline model:
Comparative Fit Index (CFI) 1.000 1.000
Tucker-Lewis Index (TLI) 1.000 1.000
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -159340.600 -159340.600
Loglikelihood unrestricted model (H1) -159338.171 -159338.171
Number of free parameters 13 13
Akaike (AIC) 318707.201 318707.201
Bayesian (BIC) 318846.759 318846.759
Sample-size adjusted Bayesian (BIC) 318805.445 318805.445
Root Mean Square Error of Approximation:
RMSEA 0.003 0.002
90 Percent Confidence Interval 0.001 0.007 0.000 0.005
P-value RMSEA <= 0.05 1.000 1.000
Robust RMSEA 0.003
90 Percent Confidence Interval 0.000 0.008
Standardized Root Mean Square Residual:
SRMR 0.000 0.000
Parameter Estimates:
Information Expected
Standard Errors Robust.sem
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
gov_trust2 =~
trust_gov 1.000 0.208 0.613
trust_parlimnt 1.015 0.003 330.156 0.000 0.211 0.653
trust_police 1.099 0.004 253.511 0.000 0.228 0.700
trust_courts 1.315 0.005 247.330 0.000 0.273 0.847
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.trust_gov ~~
.trust_parlimnt 0.029 0.000 123.012 0.000 0.029 0.438
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.trust_gov 0.484 0.001 715.884 0.000 0.484 1.428
.trust_parlimnt 0.456 0.001 703.812 0.000 0.456 1.414
.trust_police 0.536 0.001 822.959 0.000 0.536 1.644
.trust_courts 0.515 0.001 796.359 0.000 0.515 1.595
gov_trust2 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.trust_gov 0.072 0.000 259.086 0.000 0.072 0.624
.trust_parlimnt 0.060 0.000 226.846 0.000 0.060 0.573
.trust_police 0.054 0.000 218.146 0.000 0.054 0.510
.trust_courts 0.029 0.000 107.510 0.000 0.029 0.282
gov_trust2 0.043 0.000 148.215 0.000 1.000 1.000
R-Square:
Estimate
trust_gov 0.376
trust_parlimnt 0.427
trust_police 0.490
trust_courts 0.718merged4<-cbind(merged2, predict(fit_a1, newdata = merged2))
merged4$gov_trust2<-range01(merged4$gov_trust2)
# head(merged4)
merged<-plyr::rbind.fill(merged3,merged4)
merged$gov_trust<-merged$gov_trust2
head(merged)Some Recoding
table(merged$cntry,merged$year)
1582 1583 2000 2010
Albania 0 0 0 0
Algeria 0 0 0 0
Argentina 0 0 0 0
Armenia 0 0 0 0
Australia 0 0 0 0
Azerbaijan 0 0 0 0
Bahamas 0 0 0 0
Bahrain 0 0 0 0
Barbados 0 0 0 0
Belarus 0 0 0 0
Belgium 0 0 0 0
Belize 0 0 0 0
Benin 0 0 0 0
Bolivia (Plurinational State of) 0 0 0 0
Botswana 0 0 0 0
Brazil 0 0 0 0
Bulgaria 0 0 0 0
Burkina Faso 0 0 0 0
Burundi 0 0 0 0
Cabo Verde 0 0 0 0
Cambodia 0 0 0 0
Cameroon 0 0 0 0
Canada 0 0 0 1500
Chile 0 0 0 0
China 0 3 1 0
Colombia 0 0 0 0
Costa Rica 0 0 0 0
Côte D'Ivoire 0 0 0 0
Cyprus 0 0 0 0
Czech Republic 0 0 0 0
Denmark 0 0 0 0
Dominican Republic 0 0 0 0
Ecuador 0 0 0 0
El Salvador 0 0 0 0
Estonia 0 0 0 0
Finland 0 0 0 0
France 0 0 0 0
Gabon 0 0 0 0
Georgia 0 0 0 0
Germany 0 0 0 0
Ghana 0 0 0 0
Guatemala 0 0 0 0
Guinea 0 0 0 0
Guyana 0 0 0 0
Honduras 0 0 0 0
Hong Kong 0 0 0 0
Hungary 0 0 0 0
Iceland 0 0 0 0
India 0 0 0 0
Indonesia 0 0 0 0
Iraq 0 0 0 0
Ireland 0 0 0 0
Israel 0 0 0 0
Italy 0 0 0 0
Jamaica 0 0 0 0
Japan 0 0 0 2443
Jordan 0 0 0 0
Kazakhstan 0 0 0 0
Kenya 0 0 0 0
Kosovo 0 0 0 0
Kuwait 0 0 0 0
Kyrgyzstan 0 0 0 0
Lebanon 0 0 0 0
Lesotho 0 0 0 0
Liberia 0 0 0 0
Lithuania 0 0 0 0
Madagascar 0 0 0 0
Malawi 0 0 0 0
Malaysia 0 0 0 0
Mauritius 0 0 0 0
Mexico 0 0 0 0
Mongolia 0 0 0 1210
Morocco 0 0 0 0
Mozambique 0 0 0 0
Myanmar 0 0 0 0
Namibia 0 0 0 0
Netherlands 0 0 0 0
New Zealand 0 0 0 0
Nicaragua 0 0 0 0
Niger 0 0 0 0
Nigeria 0 0 0 0
Norway 0 0 0 0
Pakistan 0 0 0 0
Panama 0 0 0 0
Paraguay 0 0 0 0
Peru 0 0 0 0
Philippines 0 0 0 1200
Poland 0 0 0 0
Portugal 0 0 0 0
Qatar 0 0 0 1060
Republic of Korea 0 0 0 1200
Romania 0 0 0 0
Russian Federation 0 0 0 0
Rwanda 0 0 0 0
Sao Tome and Principe 0 0 0 0
Senegal 0 0 0 0
Sierra Leone 0 0 0 0
Singapore 1000 0 0 0
Slovakia 0 0 0 0
Slovenia 0 0 0 0
2011 2012 2013 2014
Albania 0 615 586 0
Algeria 0 0 2404 0
Argentina 0 0 2230 0
Armenia 1100 0 0 0
Australia 0 1477 0 0
Azerbaijan 1002 0 0 0
Bahamas 0 0 0 3429
Bahrain 0 0 0 1200
Barbados 0 0 0 3828
Belarus 1535 0 0 0
Belgium 0 1869 0 0
Belize 0 1512 0 1533
Benin 1200 0 0 1200
Bolivia (Plurinational State of) 0 3029 1200 0
Botswana 0 1200 0 1200
Brazil 0 1499 1204 2986
Bulgaria 0 0 2260 0
Burkina Faso 0 1200 0 0
Burundi 0 1200 0 1200
Cabo Verde 1208 0 0 1200
Cambodia 0 1200 0 0
Cameroon 0 0 1200 0
Canada 0 0 0 0
Chile 1000 0 1200 0
China 3408 2360 0 0
Colombia 0 3024 1200 1496
Costa Rica 0 1498 1000 1537
Côte D'Ivoire 0 0 1200 1199
Cyprus 1000 1089 27 0
Czech Republic 0 0 2009 0
Denmark 0 0 1650 0
Dominican Republic 0 1512 1000 1520
Ecuador 0 1500 2402 0
El Salvador 0 1497 1000 1512
Estonia 1533 2279 101 0
Finland 0 1884 313 0
France 0 0 1968 0
Gabon 0 0 0 0
Georgia 0 0 0 1202
Germany 0 2753 2251 0
Ghana 0 3952 0 2400
Guatemala 0 1509 1000 1506
Guinea 0 0 1200 0
Guyana 0 1529 0 1557
Honduras 0 1728 1000 1561
Hong Kong 0 1207 1000 0
Hungary 0 1895 119 0
Iceland 0 601 151 0
India 0 0 0 1581
Indonesia 1550 0 0 0
Iraq 0 1200 0 0
Ireland 0 995 1633 0
Israel 0 2229 279 0
Italy 0 0 960 0
Jamaica 0 1500 0 1503
Japan 1880 0 0 0
Jordan 0 0 0 1200
Kazakhstan 1500 0 0 0
Kenya 2399 0 0 2397
Kosovo 0 0 1295 0
Kuwait 0 0 0 1303
Kyrgyzstan 1500 0 0 0
Lebanon 0 0 1200 0
Lesotho 0 1197 0 1200
Liberia 0 1199 0 0
Lithuania 0 0 2109 0
Madagascar 0 0 1200 1015
Malawi 0 2407 0 2400
Malaysia 1214 1300 0 1207
Mauritius 0 1200 0 1200
Mexico 0 3560 1200 1535
Mongolia 0 0 0 1228
Morocco 1200 0 1196 0
Mozambique 0 2400 0 0
Myanmar 0 0 0 0
Namibia 0 1200 0 1200
Netherlands 0 3381 366 0
New Zealand 841 0 0 0
Nicaragua 0 1686 1000 1546
Niger 0 0 1199 0
Nigeria 1759 2364 36 2319
Norway 0 1446 178 0
Pakistan 0 1200 0 0
Panama 0 1620 1000 1508
Paraguay 0 1510 1200 1503
Peru 0 2710 1200 1500
Philippines 0 1200 0 1200
Poland 0 2851 13 0
Portugal 0 395 1756 0
Qatar 0 0 0 0
Republic of Korea 1207 0 0 0
Romania 0 1503 0 0
Russian Federation 2500 2484 0 0
Rwanda 0 1527 0 0
Sao Tome and Principe 0 0 0 0
Senegal 0 0 1200 1200
Sierra Leone 0 1190 0 0
Singapore 0 1972 0 993
Slovakia 0 1415 432 0
Slovenia 1069 1257 0 0
2015 2077
Albania 0 0
Algeria 1200 0
Argentina 1200 0
Armenia 0 0
Australia 0 0
Azerbaijan 0 0
Bahamas 0 0
Bahrain 0 0
Barbados 0 0
Belarus 0 0
Belgium 0 0
Belize 0 0
Benin 0 0
Bolivia (Plurinational State of) 1200 0
Botswana 0 0
Brazil 1250 0
Bulgaria 0 0
Burkina Faso 1200 0
Burundi 0 0
Cabo Verde 0 0
Cambodia 2400 0
Cameroon 1182 0
Canada 0 0
Chile 1200 0
China 0 1
Colombia 1200 0
Costa Rica 1000 0
Côte D'Ivoire 0 0
Cyprus 0 0
Czech Republic 0 0
Denmark 0 0
Dominican Republic 1000 0
Ecuador 1200 0
El Salvador 1000 0
Estonia 0 0
Finland 0 0
France 0 0
Gabon 1198 0
Georgia 0 0
Germany 0 0
Ghana 0 0
Guatemala 1000 0
Guinea 1200 0
Guyana 0 0
Honduras 1000 0
Hong Kong 0 0
Hungary 0 0
Iceland 0 0
India 0 0
Indonesia 0 0
Iraq 0 0
Ireland 0 0
Israel 0 0
Italy 0 0
Jamaica 0 0
Japan 0 0
Jordan 0 0
Kazakhstan 0 0
Kenya 0 0
Kosovo 0 0
Kuwait 0 0
Kyrgyzstan 0 0
Lebanon 0 0
Lesotho 0 0
Liberia 1199 0
Lithuania 0 0
Madagascar 185 0
Malawi 0 0
Malaysia 0 0
Mauritius 0 0
Mexico 1200 0
Mongolia 0 0
Morocco 1200 0
Mozambique 2400 0
Myanmar 1620 0
Namibia 0 0
Netherlands 0 0
New Zealand 0 0
Nicaragua 1000 0
Niger 1200 0
Nigeria 81 0
Norway 0 0
Pakistan 0 0
Panama 1000 0
Paraguay 1200 0
Peru 1200 0
Philippines 0 0
Poland 0 0
Portugal 0 0
Qatar 0 0
Republic of Korea 1200 0
Romania 0 0
Russian Federation 0 0
Rwanda 0 0
Sao Tome and Principe 1196 0
Senegal 0 0
Sierra Leone 1191 0
Singapore 46 0
Slovakia 0 0
Slovenia 0 0
[ reached getOption("max.print") -- omitted 24 rows ]table(subset(merged,merged$cntry=="China")$survey,
subset(merged,merged$cntry=="China")$year)
1583 2000 2011 2012 2077
asian 3 1 3408 60 1
wvs 0 0 0 2300 0merged$year[merged$cntry=="China" & merged$year == 1583] <- 2011
merged$year[merged$cntry=="China" & merged$year == 2000] <- 2011
merged$year[merged$cntry=="China" & merged$year == 2077] <- 2011
merged$year[merged$cntry=="China" & merged$year == 2012] <- 2011
table(subset(merged,merged$cntry=="Singapore")$survey,
subset(merged,merged$cntry=="Singapore")$year)
1582 2012 2014 2015
asian 1000 0 993 46
wvs 0 1972 0 0merged$year[merged$cntry=="Singapore" & merged$year == 1582] <- 2011
hist(merged$gov_trust)
Level 2 Data
V-Dem
load(vdems_start_url)
vdems_sub <- vdems_start %>% filter(year %in% 2000:2010)
#tibble(id = 1:1896)
#table(vdems_sub$country_name)
vdem <- vdems_start %>%
filter(year %in% 2000:2010) %>%
group_by(country_name) %>%
tally %>%
mutate(cntry = unique(country_name)) %>%
#DCI Variables
mutate(delib10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2xdl_delib")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(consult10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2dlconslt")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(reason10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2dlreason")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(common10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2dlcommon")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(countr10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2dlcountr")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(engage10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2dlengage")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(delibdem10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2x_delibdem")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
# Control Variables
mutate(polity10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_fh_ipolity2")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(gdp10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_GDP_Per_Cap_Haber_Men_2")) %>% #HIER MÜSSTE MAN 8 JAHRE GDP auswählen
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(riw10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_v2x_regime_ci")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(corecivil10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2xcs_ccsi")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(pop10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_mipopula")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(corruption10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2x_corr")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(polkill10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("v2x_clphy")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(educ10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_peaveduc")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
mutate(gini10 = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_peginiwi")) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
# Dummy variables
mutate(pol_round = round(polity10 * 2 -10)) %>%
mutate(polity_demdummy = ifelse(pol_round > 5, 1, 0)) %>%
mutate(polity_anodummy = ifelse(pol_round >= -5 & pol_round <= 5, 1, 0)) %>%
mutate(polity_autodummy = ifelse(pol_round < -5, 1, 0)) %>%
mutate(regime = case_when(
polity_autodummy == 1 ~ "auto",
polity_anodummy == 1 ~ "ano",
polity_demdummy == 1 ~ "demo"
)
) %>%
mutate(regime = factor(regime, levels = c("demo", "ano", "auto"))) %>%
mutate(cntry = countrycode(cntry,"country.name.en","country.name.en"))Some values were not matched unambiguously: Democratic Republic of Vietnamvdem$cntry[40] <- "Viet Nam"
# ifelse(round(vdem$polity10 * 2 -10) >= -5 & round(vdem$polity10 * 2 -10) <= 5, 1, 0)
table(vdem$regime)
demo ano auto
72 77 18 print(levels(vdem$regime)) [1] "demo" "ano" "auto" To Do - Regions
mutate(regions = vdems_sub %>%
dcast(country_name ~ year,
value.var=c("e_regionpol")) %>%
select('2000') %>%
mutate(regions = fct_recode(as.factor(`2000`),
"E. Europe and C. Asia (post-Communist)" = "1",
"Latin America & Carribean" = "2",
"MENA" = "3",
"Sub-Saharan Africa" = "4",
"W. Europe and N. America" = "5",
"South & East Asia & Pacific" = "6",
"South & East Asia & Pacific" = "7",
"South & East Asia & Pacific" = "8",
"South & East Asia & Pacific" = "9",
"Latin America & Carribean" = "10") %>%
as_factor)) %>%
select(regions)Error in as_data_frame(data) :
argument ".data" is missing, with no defaultQoG
load(qog_url)
qog10 <- qog %>%
filter(year %in% 2000:2010) %>%
mutate(cntry = countrycode(ccodecow, "cown","country.name.en"))
qog10 <- qog %>%
filter(year %in% 2000:2010) %>%
mutate(cntry = countrycode(ccodecow, "cown","country.name.en")) %>%
group_by(cntry) %>%
tally() %>%
mutate(ethnic10 = qog10 %>%
dcast(cntry ~ year,
value.var=c("al_ethnic"),
fun.aggregate = mean) %>%
select(`2000`:`2010`) %>%
rowMeans) %>%
select(-n)
# mutate(al_ethnic = as.numeric(al_ethnic)) %>%
qog10Merging Time
Level 2
level2 <- merge(x=qog10, y=vdem, by="cntry")
level2Ind + Country
combined <- merge(x=merged, y=level2, by="cntry") %>%
mutate(cntryears = paste(cntry, year))
#combined$cntryears <- paste(combined$cntry,combined$year)
vdem2 <- vdems_start %>%
filter(year %in% 2010:2015) %>%
mutate(cntry = countrycode(country_name,"country.name.en","country.name.en")) #%>% Some values were not matched unambiguously: Democratic Republic of Vietnam # select(cntry, country_name)
vdem2$cntry[996] <- "Viet Nam"
vdem2$cntry[997] <- "Viet Nam"
vdem2$cntry[998] <- "Viet Nam"
vdem2$cntry[999] <- "Viet Nam"
vdem2$cntry[1000] <- "Viet Nam"
vdem2$cntry[1001] <- "Viet Nam"
combined <- vdem2 %>%
mutate(cntryears = paste(cntry, year)) %>%
mutate(discuss_unsel = v2xcl_disc) %>%
select(cntryears, discuss_unsel) %>%
merge(combined, by = "cntryears") %>%
plyr::ddply(~cntry,
summarise,
discuss = mean(discuss_unsel, na.rm=T)) %>%
merge(combined, by = "cntry")
combinedsave(combined,file="combined.Rdata")
save(level2,file="level2.Rdata")Weighting
Macro Level
macro <- combined %>%
mutate(gov_trust = range01(gov_trust)*100) %>%
mutate(demtoday = range01(demtoday)*100) %>%
plyr::ddply(~cntry,
summarise,
mean_gov = mean(gov_trust, na.rm=T),
mean_dem = mean(demtoday, na.rm=T),
discuss = mean(discuss, na.rm=T)) %>%
# polity10 = mean(polity10, na.rm=T),
# polity_demdummy = mean(polity_demdummy, na.rm=T)) %>%
mutate(discuss_round = round(discuss*4)) %>%
mutate(mean_gov_low = case_when(
discuss_round == 2 ~ mean_gov * .9,
discuss_round == 1 ~ mean_gov * .85,
discuss_round %in% c(3,4) ~ mean_gov)) %>%
mutate(mean_gov_high = case_when(
discuss_round == 2 ~ mean_gov * .8,
discuss_round == 1 ~ mean_gov * .75,
discuss_round %in% c(3,4) ~ mean_gov)) %>%
merge(level2, by = "cntry") %>%
mutate_at(vars(common10, reason10, consult10,
countr10, engage10, engage10, delib10),
range01)
macro_dem <- macro %>%
filter(polity_demdummy == 1)
macro_aut <- macro %>%
filter(polity_demdummy == 0)
# table_stuff2<-subset(table_stuff,
# !is.na(table_stuff$regime) &
# table_stuff$cntry!="Qatar" &
# table_stuff$cntry!="Uzbekistan")
save(macro, file="macro.Rdata")
save(macro_dem, file="macro_dem.Rdata")
save(macro_aut, file="macro_aut.Rdata")Individual Level
Crap
compare_cntry<-data.frame(table(merged$cntry,merged$year))
compare_cntry<-tidyr::spread(compare_cntry,Var2,Freq)
compare_cntry[compare_cntry==0]<-100000
#compare_cntry<-na.omit(compare_cntry)
indie<-as.numeric(apply(compare_cntry[,-1],1,sum))
compare_cntry$double<-indie<900000
compare_cntry[compare_cntry==100000]<-0
compare_cntry2 <- subset(compare_cntry,compare_cntry$double==TRUE)
compare_cntry3 <- subset(merged,merged$cntry %in% compare_cntry2$Var1)
y1<-subset(compare_cntry3,compare_cntry3$cntry=="Belize" &
compare_cntry3$year==2012)$gov_trust
y2<-subset(compare_cntry3,compare_cntry3$cntry=="Belize" &
compare_cntry3$year==2014)$gov_trust
t.test(y1,y2)
y1<-subset(compare_cntry3,compare_cntry3$cntry=="Bolivia (Plurinational State of)" &
compare_cntry3$year==2012)$gov_trust
y2<-subset(compare_cntry3,compare_cntry3$cntry=="Bolivia (Plurinational State of)" &
compare_cntry3$year==2013)$gov_trust
t.test(y1,y2)
y1<-subset(compare_cntry3,compare_cntry3$cntry=="South Africa" &
compare_cntry3$year==2011)$gov_trust
y2<-subset(compare_cntry3,compare_cntry3$cntry=="South Africa" &
compare_cntry3$year==2013)$gov_trust
t.test(y1,y2)
subset(merged,merged$year==2012)
library(dplyr)
compare_cntry4 <- compare_cntry3 %>%
group_by(cntry,year) %>%
summarise_all(funs(mean(., na.rm=TRUE)))
compare_cntry4$cntryear<-paste(compare_cntry4$cntry,compare_cntry4$year)
as.data.frame(compare_cntry4[,c(17,8)])
?ddply
head(afro)
afrocntry<-unique(afro$cntry)
latinocntry<-unique(latino2013$cntry)
arabcntry<-unique(arab3$cntry)
wvscntry<-unique(wvs$cntry)
cntry_table<- data.frame(as.character(wvscntry),
c(as.character(arabcntry),rep("-",48)),
c(as.character(latinocntry),rep("-",41)),
c(as.character(afrocntry),rep("-",24)))
colnames(cntry_table) <- c("wvs","arab","latino","afro")
arrange(cntry_table, wvs, afro)
table(combined$politcat.x)
mplusdata <- combined %>% dplyr::select(cntry, delib10, demtoday,
trust_gov, trust_parliament,
trust_police,trust_courts,weight)
mplusdata <- na.omit(mplusdata)
mplusdata$cntry <- as.numeric(mplusdata$cntry)
write_csv(mplusdata, path = "mplusdata.csv", col_names = F)
mplusdata2 <- combined %>% dplyr::select(cntry, delib10, demtoday,
gov_trust,weight)
mplusdata2 <- na.omit(mplusdata2)
mplusdata2$cntry <- as.numeric(mplusdata2$cntry)
write_csv(mplusdata2, path = "mplusdat2a.csv", col_names = F)
mplusdata2_dem <- combined_dem %>% dplyr::select(cntry, delib10, demtoday,
gov_trust,weight)
mplusdata2_dem <- na.omit(mplusdata2_dem)
mplusdata2_dem$cntry <- as.numeric(mplusdata2_dem$cntry)
write_csv(mplusdata2_dem, path = "mplusdat2_dema.csv", col_names = F)
mplusdata2_aut <- combined_aut %>% dplyr::select(cntry, delib10, demtoday,
gov_trust,weight)
mplusdata2_aut <- na.omit(mplusdata2_aut)
mplusdata2_aut$cntry <- as.numeric(mplusdata2_aut$cntry)
write_csv(mplusdata2_aut, path = "mplusdat2_auta.csv", col_names = F)
combined <- merge(combined, physi2_s, by = "cntry")
combined_dem <- subset(combined,combined$polity_demdummy==1)
mplusdata_dem <- combined_dem %>% dplyr::select(cntry, delib10, demtoday,
trust_gov, trust_parliament,
trust_police,trust_courts,weight)
mplusdata_dem <- na.omit(mplusdata_dem)
mplusdata_dem$cntry <- as.numeric(mplusdata_dem$cntry)
write_csv(mplusdata_dem, path = "mplusdata_dem.csv", col_names = F)
combined_aut <- subset(combined,combined$polity_demdummy==0)
mplusdata_aut <- combined_aut %>% dplyr::select(cntry, delib10, demtoday,
trust_gov, trust_parliament,
trust_police,trust_courts,weight)
mplusdata_aut <- na.omit(mplusdata_aut)
mplusdata_aut$cntry <- as.numeric(mplusdata_aut$cntry)
write_csv(mplusdata_aut, path = "mplusdata_aut.csv", col_names = F)
#data_wids <- dcast(merged, cntry~year,
# value.var=c("year"))
#data_wids2 <- as.data.frame(lapply(data_wids[,-1],function(n) 0<n))
#data_wids3 <- as.data.frame(apply(data_wids2,2,as.numeric))
#data_wids3 <- cbind(data_wids[,1],data_wids3)
#names(data_wids3)[1]<-"cntry"
#table_stuff <- merge(data_wids3 ,table_stuff, by="cntry")
#table_stuff$polity10[table_stuff$cntry=="Tunisia"] <- 5.32382
#table_stuff2$polity10[table_stuff2$cntry=="Tunisia"] <- 5.32382
head(table_stuff)
cor(na.omit(data.frame(table_stuff$legit,table_stuff$legit2,table_stuff$legit3,
table_stuff$mean_gov)))
table_stuff$gni <- table_stuff$gni_c
table_stuff$gni_c[table_stuff$gni <= 1025] <- "low"
table_stuff$gni_c[table_stuff$gni > 1026 & table_stuff$gni <= 4035] <- "lower-middle"
table_stuff$gni_c[table_stuff$gni > 4036 & table_stuff$gni <= 12475] <- "upper-middle"
table_stuff$gni_c[table_stuff$gni > 12475] <- "high"
table_stuff$gni_c2 <- table_stuff$gni_c
table_stuff$gni_c2[table_stuff$gni_c == "lower-middle"] <- "low"
table_stuff$gni_c2[table_stuff$gni_c == "upper-middle"] <- "high"
table(table_stuff$gni_c)
table(table_stuff$gni_c2)
The cut-off points are HDI of less than 0.550
for low human development, 0.550-0.699 for medium human
development, 0.700-0.799 for high human development and
0.800 or greater for very high human development.
#table_stuff$hdi_c <- table_stuff$hdi
#table_stuff$hdi_c[table_stuff$hdi < 0.550] <- "low"
#table_stuff$hdi_c[table_stuff$hdi >= 0.550 & table_stuff$hdi <= 0.699] <- "medium"
#table_stuff$hdi_c[table_stuff$hdi >= 0.700 & table_stuff$hdi <= 0.899] <- "high"
#table_stuff$hdi_c[table_stuff$hdi >= 0.90] <- "very high"
#table(table_stuff$hdi_c)
table(round(table_stuff$terror))
table_stuff$mean_gov2 <- table_stuff$mean_gov
table_stuff$mean_gov2[is.na(table_stuff$mean_gov2)]<-999
table_stuff$mean_gov3 <- table_stuff$mean_gov
table_stuff$mean_gov3[is.na(table_stuff$mean_gov3)]<-999
table_stuff$mean_gov4 <- table_stuff$mean_gov
table_stuff$mean_gov4[is.na(table_stuff$mean_gov4)]<-999
table_stuff$mean_gov5 <- table_stuff$mean_gov
table_stuff$mean_gov5[is.na(table_stuff$mean_gov5)]<-999
table_stuff$mean_gov6 <- table_stuff$mean_gov
table_stuff$mean_gov6[is.na(table_stuff$mean_gov6)]<-999
table_stuff$mean_gov7 <- table_stuff$mean_gov
table_stuff$mean_gov7[is.na(table_stuff$mean_gov7)]<-999
table_stuff$mean_gov8 <- table_stuff$mean_gov
table_stuff$mean_gov8[is.na(table_stuff$mean_gov8)]<-999
table_stuff$mean_gov9 <- table_stuff$mean_gov
table_stuff$mean_gov9[is.na(table_stuff$mean_gov9)]<-999
table_stuff$physviol2 <- table_stuff$physviol
table_stuff$physviol2[is.na(table_stuff$physviol2)] <- 999
table_stuff$terror2 <- round(table_stuff$terror)
table_stuff$terror2[is.nan(table_stuff$terror2)] <- 999
table_stuff$discuss2 <- round(table_stuff$discuss*4)
table_stuff$discuss2[is.nan(table_stuff$discuss2)] <- 999
table(table_stuff$physviol)
table_stuff$mean_gov2[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4]-5
table_stuff$mean_gov2[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3]-10
table_stuff$mean_gov2[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2]-15
table_stuff$mean_gov2[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1]-20
table_stuff$mean_gov2[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0] -25
table_stuff$mean_gov2[table_stuff$mean_gov2>100] <- NA
table(table_stuff$mean_gov2)
table_stuff$mean_gov3[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4]-8
table_stuff$mean_gov3[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3]-16
table_stuff$mean_gov3[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2]-24
table_stuff$mean_gov3[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1] -32
table_stuff$mean_gov3[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0] -30
table_stuff$mean_gov3[table_stuff$mean_gov3>100] <- NA
table(table_stuff$mean_gov3)
table_stuff$mean_gov4[table_stuff$terror2==5] <- table_stuff$mean_gov4[table_stuff$terror2==5]-5
table_stuff$mean_gov4[table_stuff$terror2==4] <- table_stuff$mean_gov4[table_stuff$terror2==4]-10
table_stuff$mean_gov4[table_stuff$terror2==3] <- table_stuff$mean_gov4[table_stuff$terror2==3]-15
table_stuff$mean_gov4[table_stuff$mean_gov4>100] <- NA
table(table_stuff$mean_gov4)
table_stuff$mean_gov5[table_stuff$terror2==5] <- table_stuff$mean_gov5[table_stuff$terror2==5]-8
table_stuff$mean_gov5[table_stuff$terror2==4] <- table_stuff$mean_gov5[table_stuff$terror2==4]-16
table_stuff$mean_gov5[table_stuff$terror2==3] <- table_stuff$mean_gov5[table_stuff$terror2==3]-24
table_stuff$mean_gov5[table_stuff$mean_gov5>100] <- NA
table(table_stuff$mean_gov5)
table_stuff$mean_gov6[table_stuff$discuss2==2] <- table_stuff$mean_gov6[table_stuff$discuss2==2] -5
table_stuff$mean_gov6[table_stuff$discuss2==1] <- table_stuff$mean_gov6[table_stuff$discuss2==1] -10
table_stuff$mean_gov6[table_stuff$mean_gov6>100] <- NA
table(table_stuff$mean_gov6)
table_stuff$mean_gov7[table_stuff$discuss2==2] <- table_stuff$mean_gov7[table_stuff$discuss2==2] -10
table_stuff$mean_gov7[table_stuff$discuss2==1] <- table_stuff$mean_gov7[table_stuff$discuss2==1] -20
table_stuff$mean_gov7[table_stuff$mean_gov7>100] <- NA
table(table_stuff$mean_gov7)
load(qog_url)
qog10 <- subset(qog,qog$year==2000 |
qog$year==2001 |
qog$year==2002 |
qog$year==2003 |
qog$year==2004 |
qog$year==2005 |
qog$year==2006 |
qog$year==2007 |
qog$year==2008 |
qog$year==2009 |
qog$year==2010)
library(countrycode)
qog10$cntry<-countrycode(qog10$ccodecow, "cown","country.name.en")
qog10$al_ethnic<-as.numeric(qog10$al_ethnic)
#tidyr::gather(qog10,c("cntry","year"),"al_ethnic")
qog10a <- qog10 %>% dplyr::select(cntry,year,al_ethnic) %>%
as.data.frame()
data_wide17 <- reshape(data = qog10a,
idvar = "cntry",
v.names = "al_ethnic",
timevar = "year",
direction = "wide")
#qog10hdi <- qog10 %>% dplyr::select(cntry,year,undp_hdi) %>%
# as.data.frame()
#data_wide_hdi <- hdi
#reshape(data = qog10hdi,
# idvar = "cntry",
# v.names = "undp_hdi",
# timevar = "year",
# direction = "wide")
table(qog$al_ethnic,qog$year)
qog14 <- subset(qog,qog$year==2014)
qog14$cntry<-countrycode(qog14$ccodecow, "cown","country.name.en")
qog14 <- qog14 %>% dplyr::select(cntry,year,cspf_legit) %>%
as.data.frame()
data_wide18 <- reshape(data = qog14,
idvar = "cntry",
v.names = "cspf_legit",
timevar = "year",
direction = "wide")
qog15 <- subset(qog,qog$year==2015)
qog15$cntry<-countrycode(qog15$ccodecow, "cown","country.name.en")
qog15 <- qog15 %>% dplyr::select(cntry,year,ffp_sl) %>%
as.data.frame()
data_wide19 <- reshape(data = qog15,
idvar = "cntry",
v.names = "ffp_sl",
timevar = "year",
direction = "wide")
qog10$ciri_physint<-as.numeric(qog10$ciri_physint)
qog10a <- qog10 %>% dplyr::select(cntry,year,ciri_physint) %>%
as.data.frame()
data_wide20 <- reshape(data = qog10a,
idvar = "cntry",
v.names = "ciri_physint",
timevar = "year",
direction = "wide")
qog10$gd_ptss <-as.numeric(qog10$gd_ptss)
qog10a <- qog10 %>% dplyr::select(cntry,year,gd_ptss) %>%
as.data.frame()
data_wide21 <- reshape(data = qog10a,
idvar = "cntry",
v.names = "gd_ptss",
timevar = "year",
direction = "wide")
qog14<-subset(qog,qog$year==2014)
qog13<-subset(qog,qog$year==2013)
qog12<-subset(qog,qog$year==2012)
qog11<-subset(qog,qog$year==2011)
qog10<-subset(qog,qog$year==2010)
qog8<-subset(qog,qog$year==2008)
qog7<-subset(qog,qog$year==2007)
qog6<-subset(qog,qog$year==2006)
qog5<-subset(qog,qog$year==2005)
qog4<-subset(qog,qog$year==2004)
qog3<-subset(qog,qog$year==2003)
qog2<-subset(qog,qog$year==2002)
qog1<-subset(qog,qog$year==2001)
qog0<-subset(qog,qog$year==2000)
table(is.na(qog$wel_culture),qog$year)
culreg<- pmax(qog14$wel_culture, qog13$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog12$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog11$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog10$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog8$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog7$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog6$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog5$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog4$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog3$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog2$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog1$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog0$wel_culture, na.rm = TRUE)
culreg<- as.character(sjmisc::to_label(culreg))
culreg<-as.data.frame(cbind(culreg,qog14$ccodecow))
oreast <- subset(culreg, culreg$culreg == "Orthodox East")
oreast
#culreg<- pmax(culreg, qog10$wel_culture, na.rm = TRUE)
qog14$ht_region
culreg$cntry<-countrycode(culreg$V2, "cown","country.name.en")
tabeletto<-merge(table_stuff,culreg,by="cntry")
tabeletto<-as.data.frame(cbind(table_stuff$regions,
as.character(table_stuff$cntry),
as.character(tabeletto$culreg)))
tabeletto <- tabeletto[order(tabeletto$V1, tabeletto$V3),]
#edit(tabeletto)
culreg<-data.frame(cbind(tabeletto,culreg))
culreg<-culreg[,c(2,4)]
names(culreg)[1]<-c("cntry")
table_stuff3 <- merge(table_stuff,culreg,by="cntry")
table(as.character(tabeletto$V3))
qog100 <- subset(qog,qog$year==2010)
qog100$cntry<-countrycode(sjmisc::to_label(qog100$ccodecow), "cown","country.name.en")
qog100 <- qog100 %>% dplyr::select(cntry,year,ht_regtype1) %>%
as.data.frame()
qog100$regtype<-sjmisc::to_label(qog100$ht_regtype1)
qog100$year <- NULL
qog100$ht_regtype1 <- NULL
table(qog100$regtype)
ethnic10<-as.numeric(rowMeans(data_wide17[,2:12])) # mean over last 10 years (2000 - 2010)
physint10<-as.numeric(rowMeans(data_wide20[,2:12])) # mean over last 10 years (2000 - 2010)
terror10<-as.numeric(rowMeans(data_wide21[,2:12])) # mean over last 10 years (2000 - 2010)
legit<-as.numeric(data_wide18[,2]) # mean over last 10 years (2000 - 2010)
legit2<-as.numeric(data_wide19[,2]) # mean over last 10 years (2000 - 2010)
qogthingy<-data.frame(data_wide19[,1],ethnic10,legit,legit2,physint10,terror10)
colnames(qogthingy)[1]<-c("cntry")
qogthingy<-merge(x=qogthingy, y=qog100, by="cntry")
#qogthingy<-data.frame(data_wide17[,1],ethnic10)
#colnames(qogthingy)[1]<-c("cntry")
#combined <- merge(x=qogthingy, y=combined, by="cntry")
qog_cs <-read_spss("C:/Users/Favone/Downloads/qog_std_cs_jan17.sav") #loading dataset
qog_cs$cntry<-countrycode(qog_cs$ccodecow, "cown","country.name.en")
qog_cs$legit3<-as.numeric(qog_cs$gov_ixlegitimacyindex)
legit_dat<-data.frame(qog_cs$cntry,qog_cs$legit3)
colnames(legit_dat)<-c("cntry","legit3")
aggrdelib <- merge(x=legit_dat, y=aggrdelib, by="cntry")
aggrdelib$legit3 <- range01(aggrdelib$legit3)
cor(na.omit(aggrdelib[,c(4,5,7,13)]))
cor(na.omit(aggrdelib[,c(4,5,2,6:13)]))
aggrdelib$legit <- 1-range01(aggrdelib$legit)
aggrdelib$legit2 <- 1-range01(aggrdelib$legit2)
aggrdelib$asia <- aggrdelib$e.asia + aggrdelib$s.e.asia + aggrdelib$s.asia + aggrdelib$pacific
SFI <-read_spss("C:/Users/Favone/Downloads/SFIv2016.sav") #loading dataset
SFI <- subset(SFI,SFI$year==2015)
SFI$cntry <-countrycode(SFI$country, "country.name.en","country.name.en")
SFI$cntry[79] <- "North Korea"
SFI$cntry <-countrycode(SFI$cntry, "country.name.en","country.name.en")
SFI$legitimacy <- SFI$legit
SFI$legit <- NULL
aggrdelib <- merge(x=SFI, y=aggrdelib, by="cntry")
hdi <- read_csv("hdi.csv")
hdi$cntry<-countrycode(hdi$Country, "country.name.en","country.name.en")
hdi$'1990'<- NULL ; hdi$'1991'<- NULL ; hdi$'1992'<- NULL ; hdi$'1993'<- NULL
hdi$'1994'<- NULL; hdi$'1995'<- NULL; hdi$'1996'<- NULL; hdi$'1997'<- NULL
hdi$'1998'<- NULL ; hdi$'1999'<- NULL ; hdi$'2011'<- NULL ; hdi$'2012'<- NULL
hdi$'2013'<- NULL; hdi$'2014'<- NULL; hdi$'2015'<- NULL;hdi$`HDI Rank (2015)`<- NULL
hdi$Country<- NULL
hdi10<-as.numeric(rowMeans(hdi[,1:11]))
hdat<-data.frame(hdi$cntry,hdi10)
names(hdat)<-c("cntry","hdi10")
aggrdelib <- merge(x=hdat, y=aggrdelib, by="cntry")
#table(aggrdelib$hdi10)
#gc()
#combined <- merge(x=hdat, y=combined, by="cntry")
#colnames(aggrdelib)[9]<-"e_p_polity"
#lop<-subset(vdems_start,vdems_start$year==2010)
cor(na.omit(aggrdelib2[,2:17]))
cor(na.omit(data.frame(aggrdelib$polity10,aggrdelib$delib10)))
#hist(vdems$v2dlconslt)
#hist(vdems$v2xcl_disc)
#table(vdems_start$e_boix_regime,vdems_start$year)
#table(vdems_sub$e_p_polity,vdems_sub$year)
##### merging time ####
combined <- merge(x=merged, y=aggrdelib, by="cntry")
table(combined$cntry)
combined <- as.data.frame(combined)
combined$gov_trust <- as.numeric(combined$gov_trust)
combined$age <- as.numeric(combined$age)
combined$income <- as.numeric(combined$income)
combined$educ <- as.numeric(combined$educ)
combined$sex <- as.factor(combined$sex)
combined$authoritarian <- as.numeric(combined$authoritarian)
combined$safety <- as.numeric(combined$safety)
combined$demtoday <- as.numeric(combined$demtoday)
combined$latino <- factor(combined$latino)
combined$afro <- factor(combined$afro)
combined$americas <- factor(combined$americas)
combined$asia <- combined$e.asia + combined$s.e.asia + combined$s.asia + combined$pacific
cor(na.omit(data.frame(combined$gov_trust,combined$income,combined$educ, #Socioeconomic factors
combined$delib10, combined$polity10, combined$gdp10, combined$demtoday)))
combined$cntry<-as.factor(combined$cntry)
#combined$polity10 <- combined$polity10*20-10
#combined$regime <- combined$polity10
#combined$regime[combined$polity_autodummy==1] <- "auto"
#combined$regime[combined$polity_anodummy==1] <- "ano"
#combined$regime[combined$polity_demdummy==1] <- "demo"
hist(combined$gov_trust)
qplot(combined$gov_trust)
combined$cntry
#physi_s <- vdems_sub2 %>% dplyr::select(cntryears, franz)
#combined <- merge(combined, physi_s, by = "cntryears")
#physi2_s <- ddply(combined,~cntry,
# summarise,politcat=mean(franz,na.rm=T))
#combined <- merge(combined, physi2_s, by = "cntry")
vdems_sub2$cntryears <- paste(vdems_sub2$cntry,vdems_sub2$year)
physi <- vdems_sub2 %>% dplyr::select(cntryears, perc)
combined <- merge(combined, physi, by = "cntryears")
physi2 <- ddply(combined,~cntry,
summarise,physviol=mean(perc,na.rm=T))
combined <- merge(combined, physi2, by = "cntry")
#physi_22$dis3 <-round(physi_22$dis2)
#unique(physi2_s$cntry)
#1-2.5
#3-5
#5.5 - 7
#physi2_s$politcat2<-8-((physi2_s$politcat) * (7/10))
#11
#physi2_s$polity_demdummy <- physi2_s$politcat2
#physi2_s$polity_demdummy [physi2_s$politcat2 <= 2.5] <- 1
#physi2_s$polity_demdummy [physi2_s$politcat2 > 2.5] <- 0
#table(physi2_s$polity_demdummy)
#physi2_s$polity_anodummy <- physi2_s$politcat2
#physi2_s$polity_anodummy[physi2_s$politcat2 > 2.5 & physi2_s$politcat2 < 5.5] <- 1
#physi2_s$polity_anodummy[physi2_s$politcat2 <= 2.5 | physi2_s$politcat2 >= 5.5] <- 0
#table(physi2_s$polity_anodummy)
#physi2_s$polity_autodummy <- physi2_s$politcat2
#physi2_s$polity_autodummy[physi2_s$politcat2 >= 5.5] <- 1
#physi2_s$polity_autodummy[physi2_s$politcat2 < 5.5] <- 0
#table(physi2_s$polity_autodummy)
#physi2_s$regime <- physi2_s$politcat
#physi2_s$regime[physi2_s$polity_autodummy==1] <- "auto"
#physi2_s$regime[physi2_s$polity_anodummy==1] <- "ano"
#physi2_s$regime[physi2_s$polity_demdummy==1] <- "demo"
#unique(physi2_s$cntry)
table(physi2_s$regime)
qog2000 <- subset(qog,qog$year==2010 |
qog$year==2011 |
qog$year==2012 |
qog$year==2013 |
qog$year==2014 |
qog$year==2015)
table(qog2000$gd_ptsa,qog2000$year)
qog2000$perc2 <- qog2000$gd_ptsa
qog2000$cntry<-countrycode(qog2000$ccodecow, "cown","country.name.en")
qog2000$cntryears <- paste(qog2000$cntry,qog2000$year)
physi2000 <- qog2000 %>% dplyr::select(cntryears, perc2)
combined <- merge(combined, physi2000, by = "cntryears")
physi22000 <- ddply(combined,~cntry,
summarise,terror=mean(perc2,na.rm=T))
combined <- merge(combined, physi22000, by = "cntry")
#table(qog2000$undp_hdi,qog2000$year)
#physiff <- qog2000 %>% dplyr::select(cntryears, undp_hdi)
#combined <- merge(combined, physiff, by = "cntryears")
#physiff2 <- ddply(combined,~cntry,
# summarise,hdi=mean(undp_hdi,na.rm=T))
#combined <- merge(combined, physiff2, by = "cntry")
gni <- read_csv("GNI.csv", skip = 1)
gni$`2015`<-gsub("ttt","",gni$`2015`)
gni$`2015`<-gsub("ff","",gni$`2015`)
gni$`2015`<-gsub("sss","",gni$`2015`)
gni$`2015`<-gsub("uuu","",gni$`2015`)
gni$`2015`<-gsub("o","",gni$`2015`)
gni$`2015` <- as.numeric(gni$`2015`)
gni$`2014`<-gsub("ttt","",gni$`2014`)
gni$`2014`<-gsub("ff","",gni$`2014`)
gni$`2014`<-gsub("sss","",gni$`2014`)
gni$`2014`<-gsub("uuu","",gni$`2014`)
gni$`2014`<-gsub("o","",gni$`2014`)
gni$`2014` <- as.numeric(gni$`2014`)
gni<-gni[,c(2,23:28)]
gni<-gather(as.data.frame(gni),key = "Country")
names(gni) <- c("cntry","year","gni")
gni$cntry<-countrycode(gni$cntry,"country.name.en","country.name.en")
gni$cntryears <- paste(gni$cntry,gni$year)
physigni <- gni %>% dplyr::select(cntryears, gni)
combined <- merge(combined, physigni, by = "cntryears")
physigni2 <- ddply(combined,~cntry,
summarise,gni_c=mean(gni,na.rm=T))
combined <- merge(combined, physigni2, by = "cntry")
---
title: "R Notebook"
output: html_notebook
---

# Loading the Packages

```{r}
library(pacman)

#p_install_gh("favstats/favstats")
p_load(tidyverse, haven,sjmisc,
                     forcats, weights, car,
                     countrycode, lavaan,
                     semTools, lavaan.survey,
                     survey, reshape2, favstats,
                     magrittr)
```

# Loading the URLs

```{r}
afro5_url <- url("https://github.com/favstats/database_delib/raw//master/afro5.RData")
afro6_url <- url("https://github.com/favstats/database_delib/raw//master/afro6.RData")

latino2013_url <- url("https://github.com/favstats/database_delib/raw//master/latino2013.RData")
latino2015_url <- url("https://github.com/favstats/database_delib/raw//master/latino2015.RData")

wvs_raw_url <- url("https://github.com/favstats/database_delib/raw//master/wvs_raw.RData")

americas_url <- url("https://github.com/favstats/database_delib/raw//master/americas.RData")
bolivia_url <- url("https://github.com/favstats/database_delib/raw//master/bolivia.RData")
canada_url <- url("https://github.com/favstats/database_delib/raw//master/canada.RData")

asian_raw_url <- url("https://github.com/favstats/database_delib/raw//master/asian_raw.RData")
myanmar_raw_url <- url("https://github.com/favstats/database_delib/raw//master/myanmar_raw.RData")
mongolia_raw_url <- url("https://github.com/favstats/database_delib/raw//master/mongolia_raw.RData")
philip_raw_url <- url("https://github.com/favstats/database_delib/raw//master/philip_raw.RData")
taiwan_raw_url <- url("https://github.com/favstats/database_delib/raw//master/taiwan_raw.RData")
thai_raw_url <- url("https://github.com/favstats/database_delib/raw//master/thai_raw.RData")
malay_raw_url <- url("https://github.com/favstats/database_delib/raw//master/malay_raw.RData")
singapore_raw_url <- url("https://github.com/favstats/database_delib/raw//master/singapore_raw.RData")
sk_raw_url <- url("https://github.com/favstats/database_delib/raw//master/sk_raw.RData")
cambodia_raw_url <- url("https://github.com/favstats/database_delib/raw//master/cambodia_raw.RData")

ESS_raw_url <- url("https://github.com/favstats/database_delib/blob/master/ESS_raw.Rdata?raw=true")

vdems_start_url <- url("https://github.com/favstats/database_delib/raw//master/vdems_start.Rdata")

qog_url <- url("https://github.com/favstats/database_delib/raw//master/qog.Rdata")

```


# Afrobarometer Data

## Afro 5

```{r}
load(afro5_url)

delete_na_afro <- function(x) {
  x <- Recode(x, "9 = NA;
            98 = NA;
            99 = NA;
            -1 = NA")
  return(x)
} # Funktion um die NAs im Afro Datensatz zu bestimmen

# alle Variablen
afro_5 <- afro5 %>%
  rename(
    educ = Q97, # Variablen umbenennen
    income = Q3B,
    sex = Q101,
    work = Q96,
    trust_gov = Q59A,
    trust_parliament = Q59B,
    trust_police = Q59H,
    trust_courts = Q59J,
    demtoday = Q46A
  ) %>%
  mutate_at(
    vars(
      income, trust_gov, trust_parliament,
      trust_police, trust_courts, work
    ),
    delete_na_afro
  ) %>% # NAs deleten
  mutate(
    sex = sex - 1, # sex (0/1) codieren
    work = Recode(
      work, # work (0/1) codieren
      "1 = 0;
       2 = 1;
       3 = 1"
    ),
    age = Recode(
      Q1, # missing values l?schen
      "-1 = NA;
      998 = NA;
      999 = NA"
    ),
    demtoday = Recode(
      demtoday,
      "-1 = NA;
       98 = NA;
       99 = NA"
    ),
    educ = Recode(
      educ,
      "-1 = NA;
       98 = NA;
       99 = NA;
      999 = NA"
    ),
    cntry = to_label(COUNTRY_ALPHA),
    year = as.numeric(format(DATEINTR, "%Y"))
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  )

afro_5
```

## Afro 6

```{r}
load(afro6_url)

afro_6 <- afro6 %>%
  rename(
    educ = Q97,
    income = Q4B,
    sex = Q101,
    work = Q95,
    trust_gov = Q52A,
    trust_parliament = Q52B,
    trust_police = Q52H,
    trust_courts = Q52J,
    demtoday = Q40
  ) %>%
  mutate_at(
    vars(
      income, demtoday, trust_gov,
      trust_parliament, trust_police,
      trust_courts, work
    ),
    delete_na_afro
  ) %>% # NAs deleten
  mutate(
    sex = sex - 1, # sex (0/1) codieren
    work = Recode(
      work, # work (0/1) codieren
      "1 = 0;
       2 = 1;
       3 = 1"
    ),
    age = Recode(
      Q1, # missing values l?schen
      "-1 = NA;
      998 = NA;
      999 = NA"
    ),
    educ = Recode(
      educ,
      "-1 = NA;
       98 = NA;
       99 = NA;
      999 = NA"
    ),
    demtoday = Recode(
      demtoday,
      "8 = NA"
    ),
    cntry = to_label(COUNTRY_R5List),
    year = as.numeric(format(DATEINTR, "%Y"))
  ) %>%
  #  mutate_at(vars(income, demtoday, educ, age,
  #                 trust_gov, trust_parliament,
  #                 trust_police, trust_courts),
  #            as.character) %>%
  #  mutate_at(vars(income, demtoday, educ, age,
  #                 trust_gov, trust_parliament,
  #                 trust_police, trust_courts),
  #            as.numeric) %>%
  select(
    cntry, year, age, sex, income,
    educ, work, demtoday,
    trust_gov, trust_parliament,
    trust_police, trust_courts
  ) 

afro_6

```

## Merging the Data

```{r}
afro_real <- afro_5 %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )

afro_real2 <- afro_6 %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )
# length(unique(afro_real$cntry))
# length(unique(afro_real2$cntry))
# table(afro_real2$year)

afro <- rbind(afro_real, afro_real2) %>% as.tbl()

afro
```

# Latino Barometro

## Latino 2013

```{r}
load(latino2013_url)

latino_2013 <- latino2013 %>%
  rename(
    educ = REEDUC_1,
    income = S6,
    demtoday = P50TGB.A,
    age = S11
  ) %>%
  mutate(
    sex = S10 - 1, # sex variable erstellen
    cntry = to_label(IDENPA), # cntry variable erstellen
    trust_gov = 5 - P26TGB.B,
    trust_parliament = 5 - P26TGB.C,
    trust_police = 5 - P28TGB.B,
    trust_courts = 5 - P26TGB.E,
    year = 2013,
    income = 5 - income,
    work = Recode(
      S19.A,
      "2 = 1;
       3 = 1;
       4 = 0;
       5 = 0;
       6 = 0;
       7 = 0"
    )
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  )

latino_2013
```

## Latino 2015

```{r}
load(latino2015_url)

latino_2015 <- latino2015 %>%
  mutate(sex = S12 - 1) %>% # sex variable erstellen
  rename(
    educ = REEDUC_1,
    income = S4,
    demtoday = P17STGBS,
    age = S13
  ) %>%
  mutate(
    trust_gov = 5 - P16ST.G,
    trust_parliament = 5 - P16ST.F,
    trust_police = 5 - P16TGB.B,
    trust_courts = 5 - P16ST.H,
    work = Recode(
      S21.A,
      "2 = 1;
       3 = 1;
       4 = 0;
       5 = 0;
       6 = 0;
       7 = 0"
    ),
    cntry = to_label(IDENPA),
    income = 5 - income,
    year = 2015
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  )

latino_2015
```

## Merging the Data

```{r}
latino_real <- latino_2013 %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )

latino_real2 <- latino_2015 %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )

latino <- rbind(latino_real, latino_real2) %>% as.tbl()

latino

```



# World Value Survey

```{r}
load(wvs_raw_url)

wvs <- wvs_raw %>%
  rename(
    educ = V248,
    income = V239,
    demtoday = V141,
    age = V242,
    year = V262
  ) %>%
  mutate(
    sex = V240 - 1, # sex variable erstellen
    trust_gov = 5 - V115,
    trust_parliament = 5 - V117,
    trust_police = 5 - V113,
    trust_courts = 5 - V114,
    work = Recode(
      V229,
      "2 = 1;
       3 = 1;
       4 = 0;
       5 = 0;
       6 = 0;
       7 = 0;
       8 = 0"
    ),
    cntry = to_label(V2)
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday,
    trust_gov, trust_parliament,
    trust_police, trust_courts
  )



wvs
```

# Americas Barometer

## Americas Main

```{r}
load(americas_url)

americas_ <- americas %>%
  mutate(cntry = to_label(pais)) %>%
  select(
    cntry, ocup4a, ed, q10new, q1, q2,
    b10a, b13, b18, b21a, n3, year
  ) %>%
  rename(
    work = ocup4a,
    educ = ed,
    income = q10new,
    sex = q1,
    age = q2,
    trust_courts = b10a,
    trust_parliament = b13,
    trust_police = b18,
    trust_gov = b21a,
    demtoday = n3
  ) %>%
  mutate(
    sex = sex - 1, # sex variable erstellen
    work = Recode(
      work,
      "2 = 1;
       3 = 1;
       4 = 0;
       5 = 0;
       6 = 0;
       7 = 0"
    )
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  )

americas_
```


## Bolivia

```{r}
load(bolivia_url)

bolivia_ <- bolivia %>%
  mutate(cntry = to_label(pais)) %>%
  select(
    cntry, ocup4a, ed, q10new, q1, q2,
    b10a, b13, b18, b21a, n3, year
  ) %>%
  rename(
    work = ocup4a,
    educ = ed,
    income = q10new,
    sex = q1,
    age = q2,
    trust_courts = b10a,
    trust_parliament = b13,
    trust_police = b18,
    trust_gov = b21a, # viele missing values
    demtoday = n3
  ) %>%
  mutate(
    sex = sex - 1, # sex variable erstellen
    work = Recode(
      work,
      "2 = 1;
       3 = 1;
       4 = 0;
       5 = 0;
       6 = 0;
       7 = 0"
    )
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  )

bolivia_
```

## Canada

```{r}
load(canada_url)

canada_ <- canada %>%
  mutate(cntry = to_label(pais)) %>%
  select(
    cntry, exc13, education, q10, q1, q2,
    b10a, b13, b18, b21a, n3, year
  ) %>%
  rename(
    work = exc13,
    educ = education,
    income = q10,
    sex = q1,
    age = q2,
    trust_courts = b10a,
    trust_parliament = b13,
    trust_police = b18,
    trust_gov = b21a,
    demtoday = n3
  ) %>%
  mutate(
    sex = sex - 1, # sex variable erstellen
    work = work - 1,
    income = ifelse(income == 88, NA, income)
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  )

canada_
```

## Merging the Data

```{r}
americas <- americas_ %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )

bolivia <- bolivia_ %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )

canada <- canada_ %>% 
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    range01
  )


americas <- rbind(americas, bolivia, canada)

americas
```

# Asian Barometer

```{r}
load(asian_raw_url)

delete_na_asian <- function(x) {
  x <- Recode(
    x,
    "-2 = NA;
      7 = NA;
      8 = NA;
      9 = NA;
     97 = NA;
     98 = NA;
     99 = NA;
     -1 = NA"
  )

  return(x)
} # Funktion um die NAs im asian Datensatz zu bestimmen

asian_3 <- asian_raw %>%
  rename(
    educ = se5,
    trust_gov = q9,
    trust_parliament = q11,
    trust_police = q14,
    trust_courts = q8,
    sex = se2,
    income = se13a,
    work = se9
  ) %>%
  mutate_at(
    vars(
      income, trust_gov, trust_parliament,
      trust_police, trust_courts, work, sex
    ),
    delete_na_asian
  ) %>% # NAs deleten
  mutate(
    trust_gov = 5 - trust_gov,
    trust_parliament = 5 - trust_parliament,
    trust_police = 5 - trust_police,
    trust_courts = 5 - trust_courts,
    sex = sex - 1, # sex (0/1) codieren
    income = Recode(
      income,
      "0 = NA"
    ),
    income = 5 - income,
    work = Recode(
      work, # work (0/1) codieren
      "2 = 0"
    ),
    age = Recode(
      se3a, # missing values l?schen
      "-1 = NA"
    ),
    demtoday = Recode(
      q91,
      "-1 = NA;
       97 = NA;
       98 = NA;
       99 = NA"
    ),
    educ = Recode(
      educ,
      "-1 = NA;
       98 = NA;
       99 = NA"
    ),
    cntry = to_label(country),
    year = as.numeric(format(ir9, "%Y"))
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, 
    trust_gov, trust_parliament, trust_police, trust_courts
  ) 

asian_3

```

## Wave 4

```{r}
load(myanmar_raw_url)

needed <- function(data) {
  ss <- data %>%
    rename(
      educ = se5,
      trust_gov = q9,
      trust_parliament = q11,
      trust_police = q14,
      trust_courts = q8,
      sex = se2,
      income = se13a,
      work = se9
    ) %>%
    mutate_at(
      vars(
        trust_gov, trust_parliament,
        trust_police, trust_courts, work, sex
      ),
      delete_na_asian
    ) %>% # NAs deleten
    mutate(
      trust_gov = 5 - trust_gov,
      trust_parliament = 5 - trust_parliament,
      trust_police = 5 - trust_police,
      trust_courts = 5 - trust_courts,
      sex = sex - 1, # sex (0/1) codieren
      income = Recode(
        income,
        "0 = NA;
        97 = NA;
        98 = NA;
        99 = NA;
        -1 = NA"
      ),
      work = Recode(
        work, # work (0/1) codieren
        "2 = 0"
      ),
      age = Recode(
        se3_2, # missing values l?schen
        "-1 = NA"
      ),
      demtoday = Recode(
        q94,
        "-1 = NA;
         97 = NA;
         98 = NA;
         99 = NA"
      ),
      educ = Recode(
        educ,
        "-1 = NA;
         98 = NA;
         99 = NA"
      ),
      cntry = to_label(country),
      year = year
    ) %>%
    select(
      cntry, year, age, sex, income, educ, work, demtoday, 
      trust_gov, trust_parliament, trust_police, trust_courts
    )
  
  return(ss)
}
myanmar <- needed(myanmar_raw)

load(mongolia_raw_url)
load(philip_raw_url)
load(taiwan_raw_url)
load(thai_raw_url)
load(malay_raw_url)
load(singapore_raw_url)
load(sk_raw_url)
load(cambodia_raw_url)

mongolia  <- needed(mongolia_raw)
philip    <- needed(philip_raw)

thai      <- needed(thai_raw)
malay     <- needed(malay_raw)
singapore <- needed(singapore_raw) # Singapore hat extrem viele Missing values
sk        <- needed(sk_raw)
cambodia  <- needed(cambodia_raw)


```
## Sonderfall Taiwan

```{r}

# Taiwan Problem: Income only has 3 categories

taiwan <- taiwan_raw %>%
    rename(
      educ = se5,
      trust_gov = q9,
      trust_parliament = q11,
      trust_police = q14,
      trust_courts = q8,
      sex = se2,
      income = se14a,
      work = se9
    ) %>%
    mutate_at(
      vars(
        income, trust_gov, trust_parliament,
        trust_police, trust_courts, work, sex
      ),
      delete_na_asian
    ) %>% # NAs deleten
    mutate(
      trust_gov = 5 - trust_gov,
      trust_parliament = 5 - trust_parliament,
      trust_police = 5 - trust_police,
      trust_courts = 5 - trust_courts,
      sex = sex - 1, # sex (0/1) codieren
      income = Recode(
        4 - income,
        "8 = NA;
         9 = NA"
      ),
      work = Recode(
        work, # work (0/1) codieren
        "2 = 0"
      ),
      age = Recode(
        se3_2, # missing values l?schen
        "-1 = NA"
      ),
      demtoday = Recode(
        q94,
        "-1 = NA;
         97 = NA;
         98 = NA;
         99 = NA"
      ),
      educ = Recode(
        educ,
        "-1 = NA;
         98 = NA;
         99 = NA"
      ),
      cntry = to_label(country),
      year = year
    ) %>%
    select(
      cntry, year, age, sex, income, educ, work, demtoday, 
      trust_gov, trust_parliament, trust_police, trust_courts
    )

```


## Merging Data

```{r}

asian_3 %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

myanmar %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

mongolia %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

philip %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )  

taiwan %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )  

thai %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

malay %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

singapore %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

sk %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

cambodia %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

asian <- rbind(
  asian_3, myanmar, cambodia, sk, singapore, malay, thai, taiwan, philip,
  cambodia, mongolia
) %>% as.tbl()

asian
```


# European Social Survey

```{r}
load(ESS_raw_url)

ESS <- ESS_raw %>%
  rename(
    demtoday = dmcntov,
    age = agea,
    year = inwyys,
    trust_gov = trstplt,
    trust_parliament = trstprl,
    trust_police = trstplc,
    trust_courts = trstlgl
  ) %>%
  mutate(
    income = 5 - hincfel,
    educ = Recode(
      eisced,
      "55 = NA"
    ),
    work = ifelse(mnactic == 1, 1, 0),
    sex = gndr - 1, # sex variable erstellen
    cntry = to_label(cntry)
  ) %>%  
  mutate_at(
    vars(
      income, demtoday, educ,
      trust_gov, trust_parliament,
      trust_police, trust_courts
    ),
    stdz
  ) %>%
  select(
    cntry, year, age, sex, income, educ, work, demtoday, trust_gov, trust_parliament,
    trust_police, trust_courts
  )


ESS
```


# Merging Everything

```{r}

afro %<>% 
  mutate(survey = rep("afro", nrow(afro))) %>% 
  mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en")) 

latino %<>%  
  mutate(survey = rep("latino", nrow(latino))) %>% 
  mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))

americas %<>%  
  mutate(survey = rep("americas", nrow(americas))) %>% 
  mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))

asian %<>%  
  mutate(survey = rep("asian", nrow(asian))) %>% 
  mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en"))

wvs %<>%  
  mutate(survey = rep("wvs", nrow(wvs))) %>% 
  mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en")) %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

ESS %<>%  
  mutate(survey = rep("ESS", nrow(ESS))) %>% 
  mutate(cntry = countrycode(cntry, "country.name.en", "country.name.en")) %<>%
    mutate_at(
      vars(
        income, demtoday, educ,
        trust_gov, trust_parliament,
        trust_police, trust_courts
      ),
      range01
    )

unique(ESS$cntry)
unique(asian$cntry)
unique(wvs$cntry)
unique(latino$cntry)
unique(americas$cntry)
unique(afro$cntry)

merged <- rbind(wvs, latino, afro, americas, asian, ESS)

merged %<>%
  # create dummies
  mutate(
    wvs = ifelse(survey == "wvs", 1, 0),
    afro = ifelse(survey == "afro", 1, 0),
    latino = ifelse(survey == "latino", 1, 0),
    americas = ifelse(survey == "americas", 1, 0),
    asian = ifelse(survey == "asian", 1, 0),
    ESS = ifelse(survey == "ESS", 1, 0)
  ) %>%
  # filter bad countries
  filter(cntry != "Egypt") %>% # exclude Egypt 2013
  filter(cntry != "Libya") %>% # exclude Libya 2014
  filter(cntry != "Mali") %>% # exclude Mali 2012
  filter(cntry != "Yemen") %>% # exclude Yemen 2012
  filter(cntry != "Palestine, State of") # exclude Palestine 2013

# adding weight
merged %<>%
  group_by(cntry) %>%
  tally() %>%
  mutate(weight = 1000 / n) %>%
  select(cntry, weight) %>%
  left_join(merged, "cntry")
# select(cntry, year) %>%
# unique %>%
# View

merged

table(merged$wvs)
table(merged$afro)
table(merged$latino)
table(merged$americas)
table(merged$asian)
table(merged$ESS)
```



## SEM Index

```{r}
merged2 <- merged %>%
  mutate(gov_trust = trust_gov + trust_parliament + 
           trust_police + trust_courts) %>%
  filter(!is.na(gov_trust))

merged3 <- merged %>%
  mutate(gov_trust = trust_gov + trust_parliament + 
           trust_police + trust_courts) %>%
  filter(is.na(gov_trust))

svy.df <- survey::svydesign(id= ~1,
                              weights= ~weight,
                              data= merged) 

model <- '# measurement model 1
gov_trust2 =~ 1*trust_gov + trust_parliament + 
trust_police + trust_courts
trust_gov ~~ trust_parliament
'

merged <- merged %>%
  mutate_at(vars(trust_gov, trust_parliament,
                 trust_police, trust_courts), as.numeric)

# cor(na.omit(data.frame(merged$trust_gov,
#               merged$trust_police,
#               merged$trust_courts,
#               merged$trust_parliament,
#               merged$demtoday)))

lavaan_model1<-cfa(model, meanstructure = T, 
                   data = as.data.frame(merged),
                   estimator= "MLM")

fit_a1<-lavaan.survey(lavaan_model1, 
            estimator= "MLM", survey.design=svy.df)
summary(fit_a1, standardized=TRUE,fit.measures = TRUE, rsq = T)

merged4<-cbind(merged2, predict(fit_a1, newdata = merged2))
merged4$gov_trust2<-range01(merged4$gov_trust2)

# head(merged4)

merged<-plyr::rbind.fill(merged3,merged4)
merged$gov_trust<-merged$gov_trust2
head(merged)
```


## Some Recoding

```{r}
table(merged$cntry,merged$year)

table(subset(merged,merged$cntry=="China")$survey,
      subset(merged,merged$cntry=="China")$year)

merged$year[merged$cntry=="China" & merged$year == 1583] <- 2011
merged$year[merged$cntry=="China" & merged$year == 2000] <- 2011
merged$year[merged$cntry=="China" & merged$year == 2077] <- 2011
merged$year[merged$cntry=="China" & merged$year == 2012] <- 2011

table(subset(merged,merged$cntry=="Singapore")$survey,
      subset(merged,merged$cntry=="Singapore")$year)

merged$year[merged$cntry=="Singapore" & merged$year == 1582] <- 2011

hist(merged$gov_trust)
```




# Level 2 Data

## V-Dem

```{r}
load(vdems_start_url)

vdems_sub <- vdems_start %>% filter(year %in% 2000:2010)

#tibble(id = 1:1896)
#table(vdems_sub$country_name)

vdem <- vdems_start %>%
  filter(year %in% 2000:2010) %>% 
  group_by(country_name) %>%
  tally %>%
  mutate(cntry = unique(country_name)) %>%
  #DCI Variables
  mutate(delib10 = vdems_sub %>%
           dcast(country_name ~ year, 
                 value.var=c("v2xdl_delib")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(consult10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2dlconslt")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(reason10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2dlreason")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(common10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2dlcommon")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(countr10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2dlcountr")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(engage10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2dlengage")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(delibdem10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2x_delibdem")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  # Control Variables
  mutate(polity10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_fh_ipolity2")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(gdp10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_GDP_Per_Cap_Haber_Men_2")) %>% #HIER MÜSSTE MAN 8 JAHRE GDP auswählen
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(riw10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_v2x_regime_ci")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(corecivil10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2xcs_ccsi")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(pop10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_mipopula")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(corruption10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2x_corr")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(polkill10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("v2x_clphy")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(educ10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_peaveduc")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>%
  mutate(gini10 = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_peginiwi")) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>% 
  # Dummy variables
  mutate(pol_round = round(polity10 * 2 -10)) %>% 
  mutate(polity_demdummy = ifelse(pol_round > 5, 1, 0)) %>% 
  mutate(polity_anodummy = ifelse(pol_round >= -5 & pol_round <= 5, 1, 0)) %>% 
  mutate(polity_autodummy = ifelse(pol_round < -5, 1, 0)) %>% 
  mutate(regime = case_when(
    polity_autodummy == 1 ~ "auto",
    polity_anodummy == 1 ~ "ano",
    polity_demdummy == 1 ~ "demo"
    )
  ) %>%
  mutate(regime = factor(regime, levels = c("demo", "ano", "auto"))) %>% 
  mutate(cntry = countrycode(cntry,"country.name.en","country.name.en"))

vdem$cntry[40] <- "Viet Nam"


# ifelse(round(vdem$polity10 * 2 -10) >= -5 & round(vdem$polity10 * 2 -10) <= 5, 1, 0)

table(vdem$regime)
print(levels(vdem$regime)) 



  
```

### To Do - Regions

```{r}
  mutate(regions = vdems_sub %>% 
           dcast(country_name ~ year, 
                 value.var=c("e_regionpol")) %>%
           select('2000') %>% 
           mutate(regions = fct_recode(as.factor(`2000`),
                                "E. Europe and C. Asia (post-Communist)" = "1",
                                "Latin America & Carribean" = "2",
                                "MENA" = "3",
                                "Sub-Saharan Africa" = "4",
                                "W. Europe and N. America" = "5",
                                "South & East Asia & Pacific" = "6",
                                "South & East Asia & Pacific" = "7",
                                "South & East Asia & Pacific" = "8",
                                "South & East Asia & Pacific" = "9",
                                "Latin America & Carribean" = "10")  %>%
                                as_factor))  %>%
           select(regions)


table(regions)

regions2 <- vdems_sub %>% 
  dcast(country_name ~ year, 
        value.var=c("e_regionpol")) %>%
  select('2000') %>% 
  transmute(fct_recode(as.factor(`2000`),
                       "E. Europe and C. Asia (post-Communist)" = "1",
                       "Latin America & Carribean" = "2",
                       "MENA" = "3",
                       "Sub-Saharan Africa" = "4",
                       "W. Europe and N. America" = "5",
                       "East Asia" = "6",
                       "South-East Asia" = "7",
                       "South Asia" = "8",
                       "Pacific" = "9",
                       "Latin America & Carribean" = "10"))

table(regions2)

regions3 <- dcast(vdems_sub, country_name ~ year, value.var=c("e_regionpol"))$"2000"

postcom   <- as.numeric(regions3 == 1)
latin     <- as.numeric(regions3 == 2 | regions3 == 10)
mena      <- as.numeric(regions3 == 3)
subsahara <- as.numeric(regions3 == 4)
west      <- as.numeric(regions3 == 5)
e.asia    <- as.numeric(regions3 == 6)
s.e.asia  <- as.numeric(regions3 == 7)
s.asia    <- as.numeric(regions3 == 8)
pacific   <- as.numeric(regions3 == 9)


ww <- vdems_sub %>% 
  dcast(country_name ~ year, 
                 value.var=c("e_regionpol")) %>%
  select('2000') %>% 
  mutate(regions = fct_recode(as.factor(`2000`),
                                "E. Europe and C. Asia (post-Communist)" = "1",
                                "Latin America & Carribean" = "2",
                                "MENA" = "3",
                                "Sub-Saharan Africa" = "4",
                                "W. Europe and N. America" = "5",
                                "South & East Asia & Pacific" = "6",
                                "South & East Asia & Pacific" = "7",
                                "South & East Asia & Pacific" = "8",
                                "South & East Asia & Pacific" = "9",
                                "Latin America & Carribean" = "10")) %>%
                                 as_factor() %>%
                                 select(regions)

ww

regions2 <- vdems_sub %>% 
  dcast(country_name ~ year, 
        value.var=c("e_regionpol")) %>%
  select('2000') %>% 
  transmute(fct_recode(as.factor(`2000`),
                       "E. Europe and C. Asia (post-Communist)" = "1",
                       "Latin America & Carribean" = "2",
                       "MENA" = "3",
                       "Sub-Saharan Africa" = "4",
                       "W. Europe and N. America" = "5",
                       "East Asia" = "6",
                       "South-East Asia" = "7",
                       "South Asia" = "8",
                       "Pacific" = "9",
                       "Latin America & Carribean" = "10"))

table(regions2)

regions3 <- dcast(vdems_sub, country_name ~ year, value.var=c("e_regionpol"))$"2000"

postcom   <- as.numeric(regions3 == 1)
latin     <- as.numeric(regions3 == 2 | regions3 == 10)
mena      <- as.numeric(regions3 == 3)
subsahara <- as.numeric(regions3 == 4)
west      <- as.numeric(regions3 == 5)
e.asia    <- as.numeric(regions3 == 6)
s.e.asia  <- as.numeric(regions3 == 7)
s.asia    <- as.numeric(regions3 == 8)
pacific   <- as.numeric(regions3 == 9)



```


## QoG

```{r}
load(qog_url)

qog10 <- qog %>% 
  filter(year %in% 2000:2010) %>% 
  mutate(cntry = countrycode(ccodecow, "cown","country.name.en"))

qog10 <- qog %>% 
  filter(year %in% 2000:2010) %>% 
  mutate(cntry = countrycode(ccodecow, "cown","country.name.en")) %>% 
  group_by(cntry) %>%
  tally() %>% 
  mutate(ethnic10 = qog10 %>%
           dcast(cntry ~ year, 
                 value.var=c("al_ethnic"), 
          fun.aggregate = mean) %>%
           select(`2000`:`2010`) %>%
           rowMeans) %>% 
  select(-n)
#  mutate(al_ethnic = as.numeric(al_ethnic)) %>% 

qog10



```

## Merging Time

### Level 2

```{r}
level2 <- merge(x=qog10, y=vdem, by="cntry")

level2
```

### Ind + Country

```{r}

combined <- merge(x=merged, y=level2, by="cntry") %>% 
              mutate(cntryears = paste(cntry, year))

#combined$cntryears <- paste(combined$cntry,combined$year)


vdem2 <- vdems_start %>%
  filter(year %in% 2010:2015) %>% 
  mutate(cntry = countrycode(country_name,"country.name.en","country.name.en")) #%>% 
 # select(cntry, country_name)

vdem2$cntry[996] <- "Viet Nam"
vdem2$cntry[997] <- "Viet Nam"
vdem2$cntry[998] <- "Viet Nam"
vdem2$cntry[999] <- "Viet Nam"
vdem2$cntry[1000] <- "Viet Nam"
vdem2$cntry[1001] <- "Viet Nam"

combined <- vdem2 %>% 
  mutate(cntryears = paste(cntry, year)) %>% 
  mutate(discuss_unsel = v2xcl_disc) %>% 
  select(cntryears, discuss_unsel) %>% 
  merge(combined, by = "cntryears") %>% 
                  plyr::ddply(~cntry,
                        summarise, 
                        discuss = mean(discuss_unsel, na.rm=T)) %>% 
  merge(combined, by = "cntry")


combined


save(combined,file="combined.Rdata")
save(level2,file="level2.Rdata")
```


# Weighting

## Macro Level

```{r}

macro  <- combined %>% 
                mutate(gov_trust = range01(gov_trust)*100) %>% 
                mutate(demtoday = range01(demtoday)*100) %>% 
                plyr::ddply(~cntry,
                     summarise,
                     mean_gov = mean(gov_trust, na.rm=T),
                     mean_dem = mean(demtoday, na.rm=T),
                     discuss = mean(discuss, na.rm=T)) %>% 
#                     polity10 = mean(polity10, na.rm=T),
#                     polity_demdummy = mean(polity_demdummy, na.rm=T)) %>% 
                mutate(discuss_round = round(discuss*4)) %>% 
                mutate(mean_gov_low = case_when(
                      discuss_round == 2 ~ mean_gov * .9,
                      discuss_round == 1 ~ mean_gov * .85,
                      discuss_round %in% c(3,4) ~ mean_gov)) %>% 
                mutate(mean_gov_high = case_when(
                      discuss_round == 2 ~ mean_gov * .8,
                      discuss_round == 1 ~ mean_gov * .75,
                      discuss_round %in% c(3,4) ~ mean_gov)) %>% 
                merge(level2, by = "cntry") %>% 
                mutate_at(vars(common10, reason10, consult10, 
                 countr10, engage10, engage10, delib10), 
                                          range01)

macro_dem <- macro %>% 
                      filter(polity_demdummy == 1)

macro_aut <- macro %>% 
                      filter(polity_demdummy == 0)

# table_stuff2<-subset(table_stuff,
#                      !is.na(table_stuff$regime) &
#                        table_stuff$cntry!="Qatar" &
#                       table_stuff$cntry!="Uzbekistan")


save(macro, file="macro.Rdata")
save(macro_dem, file="macro_dem.Rdata")
save(macro_aut, file="macro_aut.Rdata")

```

## Individual Level

```{r}
combined  <- combined %>% 
                mutate(discuss_round = round(discuss*4)) %>% 
                mutate(gov_trust_low = case_when(
                      discuss_round == 2 ~ gov_trust * .9,
                      discuss_round == 1 ~ gov_trust * .85,
                      discuss_round %in% c(3,4) ~ gov_trust)) %>% 
                mutate(gov_trust_high = case_when(
                      discuss_round == 2 ~ gov_trust * .8,
                      discuss_round == 1 ~ gov_trust * .75,
                      discuss_round %in% c(3,4) ~ gov_trust)) %>% 
                mutate_at(vars(common10, reason10, consult10, 
                 countr10, engage10, engage10, delib10), 
                                          range01)

save(combined,file="combined.Rdata")

```


# Crap

```{r}

compare_cntry<-data.frame(table(merged$cntry,merged$year))
compare_cntry<-tidyr::spread(compare_cntry,Var2,Freq)
compare_cntry[compare_cntry==0]<-100000
#compare_cntry<-na.omit(compare_cntry)
indie<-as.numeric(apply(compare_cntry[,-1],1,sum))
compare_cntry$double<-indie<900000
compare_cntry[compare_cntry==100000]<-0
compare_cntry2 <- subset(compare_cntry,compare_cntry$double==TRUE)

compare_cntry3 <- subset(merged,merged$cntry %in% compare_cntry2$Var1)

y1<-subset(compare_cntry3,compare_cntry3$cntry=="Belize" &
                      compare_cntry3$year==2012)$gov_trust

y2<-subset(compare_cntry3,compare_cntry3$cntry=="Belize" &
         compare_cntry3$year==2014)$gov_trust

t.test(y1,y2)

y1<-subset(compare_cntry3,compare_cntry3$cntry=="Bolivia (Plurinational State of)" &
             compare_cntry3$year==2012)$gov_trust

y2<-subset(compare_cntry3,compare_cntry3$cntry=="Bolivia (Plurinational State of)" &
             compare_cntry3$year==2013)$gov_trust

t.test(y1,y2)

y1<-subset(compare_cntry3,compare_cntry3$cntry=="South Africa" &
             compare_cntry3$year==2011)$gov_trust

y2<-subset(compare_cntry3,compare_cntry3$cntry=="South Africa" &
             compare_cntry3$year==2013)$gov_trust

t.test(y1,y2)

       
subset(merged,merged$year==2012)

library(dplyr)
compare_cntry4 <- compare_cntry3 %>%
  group_by(cntry,year) %>%
  summarise_all(funs(mean(., na.rm=TRUE)))

compare_cntry4$cntryear<-paste(compare_cntry4$cntry,compare_cntry4$year)
  
as.data.frame(compare_cntry4[,c(17,8)])
  
?ddply
head(afro)

afrocntry<-unique(afro$cntry)
latinocntry<-unique(latino2013$cntry)
arabcntry<-unique(arab3$cntry)
wvscntry<-unique(wvs$cntry)

cntry_table<- data.frame(as.character(wvscntry),
                         c(as.character(arabcntry),rep("-",48)),
                         c(as.character(latinocntry),rep("-",41)),
                         c(as.character(afrocntry),rep("-",24)))

colnames(cntry_table) <- c("wvs","arab","latino","afro")

arrange(cntry_table, wvs, afro)



table(combined$politcat.x)
mplusdata <- combined %>% dplyr::select(cntry, delib10, demtoday, 
                                        trust_gov, trust_parliament, 
                                        trust_police,trust_courts,weight)
mplusdata <- na.omit(mplusdata)
mplusdata$cntry <- as.numeric(mplusdata$cntry)
write_csv(mplusdata, path = "mplusdata.csv", col_names = F)

mplusdata2 <- combined %>% dplyr::select(cntry, delib10, demtoday, 
                                        gov_trust,weight)
mplusdata2 <- na.omit(mplusdata2)
mplusdata2$cntry <- as.numeric(mplusdata2$cntry)
write_csv(mplusdata2, path = "mplusdat2a.csv", col_names = F)

mplusdata2_dem <- combined_dem %>% dplyr::select(cntry, delib10, demtoday, 
                                         gov_trust,weight)
mplusdata2_dem <- na.omit(mplusdata2_dem)
mplusdata2_dem$cntry <- as.numeric(mplusdata2_dem$cntry)
write_csv(mplusdata2_dem, path = "mplusdat2_dema.csv", col_names = F)

mplusdata2_aut <- combined_aut %>% dplyr::select(cntry, delib10, demtoday, 
                                         gov_trust,weight)
mplusdata2_aut <- na.omit(mplusdata2_aut)
mplusdata2_aut$cntry <- as.numeric(mplusdata2_aut$cntry)
write_csv(mplusdata2_aut, path = "mplusdat2_auta.csv", col_names = F)

combined <- merge(combined, physi2_s, by = "cntry")

combined_dem <- subset(combined,combined$polity_demdummy==1)

mplusdata_dem <- combined_dem %>% dplyr::select(cntry, delib10, demtoday, 
                                        trust_gov, trust_parliament, 
                                        trust_police,trust_courts,weight)
mplusdata_dem <- na.omit(mplusdata_dem)
mplusdata_dem$cntry <- as.numeric(mplusdata_dem$cntry)
write_csv(mplusdata_dem, path = "mplusdata_dem.csv", col_names = F)


combined_aut <- subset(combined,combined$polity_demdummy==0)

mplusdata_aut <- combined_aut %>% dplyr::select(cntry, delib10, demtoday, 
                                                trust_gov, trust_parliament, 
                                                trust_police,trust_courts,weight)
mplusdata_aut <- na.omit(mplusdata_aut)
mplusdata_aut$cntry <- as.numeric(mplusdata_aut$cntry)
write_csv(mplusdata_aut, path = "mplusdata_aut.csv", col_names = F)


#data_wids <- dcast(merged, cntry~year, 
#                    value.var=c("year"))
#data_wids2 <- as.data.frame(lapply(data_wids[,-1],function(n) 0<n))
#data_wids3 <- as.data.frame(apply(data_wids2,2,as.numeric))
#data_wids3 <- cbind(data_wids[,1],data_wids3)
#names(data_wids3)[1]<-"cntry"
#table_stuff <- merge(data_wids3 ,table_stuff, by="cntry")

#table_stuff$polity10[table_stuff$cntry=="Tunisia"] <- 5.32382
#table_stuff2$polity10[table_stuff2$cntry=="Tunisia"] <- 5.32382

head(table_stuff)
cor(na.omit(data.frame(table_stuff$legit,table_stuff$legit2,table_stuff$legit3,
                       table_stuff$mean_gov)))

table_stuff$gni <- table_stuff$gni_c

table_stuff$gni_c[table_stuff$gni <= 1025] <- "low"
table_stuff$gni_c[table_stuff$gni > 1026 & table_stuff$gni <= 4035] <- "lower-middle"
table_stuff$gni_c[table_stuff$gni > 4036 & table_stuff$gni <= 12475] <- "upper-middle"
table_stuff$gni_c[table_stuff$gni > 12475] <- "high"

table_stuff$gni_c2 <- table_stuff$gni_c

table_stuff$gni_c2[table_stuff$gni_c == "lower-middle"] <- "low"
table_stuff$gni_c2[table_stuff$gni_c == "upper-middle"] <- "high"

table(table_stuff$gni_c)
table(table_stuff$gni_c2)

The cut-off points are HDI of less than 0.550
for low human development, 0.550-0.699 for medium human
development, 0.700-0.799 for high human development and
0.800 or greater for very high human development.

#table_stuff$hdi_c <- table_stuff$hdi

#table_stuff$hdi_c[table_stuff$hdi < 0.550] <- "low"
#table_stuff$hdi_c[table_stuff$hdi >= 0.550 & table_stuff$hdi <= 0.699] <- "medium"
#table_stuff$hdi_c[table_stuff$hdi >= 0.700 & table_stuff$hdi <= 0.899] <- "high"
#table_stuff$hdi_c[table_stuff$hdi >= 0.90] <- "very high"

#table(table_stuff$hdi_c)



table(round(table_stuff$terror))

table_stuff$mean_gov2 <- table_stuff$mean_gov
table_stuff$mean_gov2[is.na(table_stuff$mean_gov2)]<-999

table_stuff$mean_gov3 <- table_stuff$mean_gov
table_stuff$mean_gov3[is.na(table_stuff$mean_gov3)]<-999

table_stuff$mean_gov4 <- table_stuff$mean_gov
table_stuff$mean_gov4[is.na(table_stuff$mean_gov4)]<-999

table_stuff$mean_gov5 <- table_stuff$mean_gov
table_stuff$mean_gov5[is.na(table_stuff$mean_gov5)]<-999

table_stuff$mean_gov6 <- table_stuff$mean_gov
table_stuff$mean_gov6[is.na(table_stuff$mean_gov6)]<-999

table_stuff$mean_gov7 <- table_stuff$mean_gov
table_stuff$mean_gov7[is.na(table_stuff$mean_gov7)]<-999

table_stuff$mean_gov8 <- table_stuff$mean_gov
table_stuff$mean_gov8[is.na(table_stuff$mean_gov8)]<-999

table_stuff$mean_gov9 <- table_stuff$mean_gov
table_stuff$mean_gov9[is.na(table_stuff$mean_gov9)]<-999

table_stuff$physviol2 <- table_stuff$physviol
table_stuff$physviol2[is.na(table_stuff$physviol2)] <- 999

table_stuff$terror2 <- round(table_stuff$terror)
table_stuff$terror2[is.nan(table_stuff$terror2)] <- 999

table_stuff$discuss2 <- round(table_stuff$discuss*4)
table_stuff$discuss2[is.nan(table_stuff$discuss2)] <- 999



table(table_stuff$physviol)
table_stuff$mean_gov2[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4]-5
table_stuff$mean_gov2[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3]-10
table_stuff$mean_gov2[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2]-15
table_stuff$mean_gov2[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1] <- table_stuff$mean_gov2[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1]-20
table_stuff$mean_gov2[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0]  <- table_stuff$mean_gov2[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0]  -25

table_stuff$mean_gov2[table_stuff$mean_gov2>100] <- NA
table(table_stuff$mean_gov2)

table_stuff$mean_gov3[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.5 & table_stuff$physviol2>0.4]-8
table_stuff$mean_gov3[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.4 & table_stuff$physviol2>0.3]-16
table_stuff$mean_gov3[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.3 & table_stuff$physviol2>0.2]-24
table_stuff$mean_gov3[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1] <- table_stuff$mean_gov3[table_stuff$physviol2<=0.2 & table_stuff$physviol2>0.1] -32
table_stuff$mean_gov3[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0] <-  table_stuff$mean_gov3[table_stuff$physviol2<=0.1 & table_stuff$physviol2>=0]  -30

table_stuff$mean_gov3[table_stuff$mean_gov3>100] <- NA
table(table_stuff$mean_gov3)

table_stuff$mean_gov4[table_stuff$terror2==5] <- table_stuff$mean_gov4[table_stuff$terror2==5]-5
table_stuff$mean_gov4[table_stuff$terror2==4] <- table_stuff$mean_gov4[table_stuff$terror2==4]-10
table_stuff$mean_gov4[table_stuff$terror2==3] <- table_stuff$mean_gov4[table_stuff$terror2==3]-15

table_stuff$mean_gov4[table_stuff$mean_gov4>100] <- NA
table(table_stuff$mean_gov4)

table_stuff$mean_gov5[table_stuff$terror2==5] <- table_stuff$mean_gov5[table_stuff$terror2==5]-8
table_stuff$mean_gov5[table_stuff$terror2==4] <- table_stuff$mean_gov5[table_stuff$terror2==4]-16
table_stuff$mean_gov5[table_stuff$terror2==3] <- table_stuff$mean_gov5[table_stuff$terror2==3]-24

table_stuff$mean_gov5[table_stuff$mean_gov5>100] <- NA
table(table_stuff$mean_gov5)

table_stuff$mean_gov6[table_stuff$discuss2==2] <- table_stuff$mean_gov6[table_stuff$discuss2==2] -5
table_stuff$mean_gov6[table_stuff$discuss2==1] <- table_stuff$mean_gov6[table_stuff$discuss2==1] -10

table_stuff$mean_gov6[table_stuff$mean_gov6>100] <- NA
table(table_stuff$mean_gov6)

table_stuff$mean_gov7[table_stuff$discuss2==2] <- table_stuff$mean_gov7[table_stuff$discuss2==2] -10
table_stuff$mean_gov7[table_stuff$discuss2==1] <- table_stuff$mean_gov7[table_stuff$discuss2==1] -20

table_stuff$mean_gov7[table_stuff$mean_gov7>100] <- NA
table(table_stuff$mean_gov7)


load(qog_url)

qog10 <- subset(qog,qog$year==2000 |
                  qog$year==2001 |  
                  qog$year==2002 | 
                  qog$year==2003 | 
                  qog$year==2004 | 
                  qog$year==2005 | 
                  qog$year==2006 | 
                  qog$year==2007 | 
                  qog$year==2008 | 
                  qog$year==2009 |
                  qog$year==2010)

library(countrycode)
qog10$cntry<-countrycode(qog10$ccodecow, "cown","country.name.en")
qog10$al_ethnic<-as.numeric(qog10$al_ethnic)
#tidyr::gather(qog10,c("cntry","year"),"al_ethnic")

qog10a <- qog10 %>% dplyr::select(cntry,year,al_ethnic) %>%
  as.data.frame()
data_wide17 <- reshape(data = qog10a,
                       idvar = "cntry",
                       v.names = "al_ethnic",
                       timevar = "year",
                       direction = "wide")

#qog10hdi <- qog10 %>% dplyr::select(cntry,year,undp_hdi) %>%
#  as.data.frame()
#data_wide_hdi <- hdi
  #reshape(data = qog10hdi,
                      # idvar = "cntry",
                      # v.names = "undp_hdi",
                      # timevar = "year",
                      # direction = "wide")



table(qog$al_ethnic,qog$year)

qog14 <- subset(qog,qog$year==2014)
qog14$cntry<-countrycode(qog14$ccodecow, "cown","country.name.en")
qog14 <- qog14 %>% dplyr::select(cntry,year,cspf_legit) %>%
  as.data.frame()
data_wide18 <- reshape(data = qog14,
                       idvar = "cntry",
                       v.names = "cspf_legit",
                       timevar = "year",
                       direction = "wide")

qog15 <- subset(qog,qog$year==2015)
qog15$cntry<-countrycode(qog15$ccodecow, "cown","country.name.en")
qog15 <- qog15 %>% dplyr::select(cntry,year,ffp_sl) %>%
  as.data.frame()
data_wide19 <- reshape(data = qog15,
                       idvar = "cntry",
                       v.names = "ffp_sl",
                       timevar = "year",
                       direction = "wide")


qog10$ciri_physint<-as.numeric(qog10$ciri_physint)
qog10a <- qog10 %>% dplyr::select(cntry,year,ciri_physint) %>%
  as.data.frame()
data_wide20 <- reshape(data = qog10a,
                       idvar = "cntry",
                       v.names = "ciri_physint",
                       timevar = "year",
                       direction = "wide")

qog10$gd_ptss <-as.numeric(qog10$gd_ptss)
qog10a <- qog10 %>% dplyr::select(cntry,year,gd_ptss) %>%
  as.data.frame()
data_wide21 <- reshape(data = qog10a,
                       idvar = "cntry",
                       v.names = "gd_ptss",
                       timevar = "year",
                       direction = "wide")

qog14<-subset(qog,qog$year==2014)
qog13<-subset(qog,qog$year==2013)
qog12<-subset(qog,qog$year==2012)
qog11<-subset(qog,qog$year==2011)
qog10<-subset(qog,qog$year==2010)
qog8<-subset(qog,qog$year==2008)
qog7<-subset(qog,qog$year==2007)
qog6<-subset(qog,qog$year==2006)
qog5<-subset(qog,qog$year==2005)
qog4<-subset(qog,qog$year==2004)
qog3<-subset(qog,qog$year==2003)
qog2<-subset(qog,qog$year==2002)
qog1<-subset(qog,qog$year==2001)
qog0<-subset(qog,qog$year==2000)



table(is.na(qog$wel_culture),qog$year)
culreg<- pmax(qog14$wel_culture, qog13$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog12$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog11$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog10$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog8$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog7$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog6$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog5$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog4$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog3$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog2$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog1$wel_culture, na.rm = TRUE)
culreg<- pmax(culreg, qog0$wel_culture, na.rm = TRUE)
culreg<- as.character(sjmisc::to_label(culreg))
culreg<-as.data.frame(cbind(culreg,qog14$ccodecow))
oreast  <- subset(culreg, culreg$culreg == "Orthodox East")
oreast

#culreg<- pmax(culreg, qog10$wel_culture, na.rm = TRUE)

qog14$ht_region 

culreg$cntry<-countrycode(culreg$V2, "cown","country.name.en")
tabeletto<-merge(table_stuff,culreg,by="cntry")
tabeletto<-as.data.frame(cbind(table_stuff$regions,
                               as.character(table_stuff$cntry),
                               as.character(tabeletto$culreg)))
tabeletto <- tabeletto[order(tabeletto$V1, tabeletto$V3),]
#edit(tabeletto)

culreg<-data.frame(cbind(tabeletto,culreg))
culreg<-culreg[,c(2,4)]
names(culreg)[1]<-c("cntry")

table_stuff3 <- merge(table_stuff,culreg,by="cntry")

table(as.character(tabeletto$V3))

qog100 <- subset(qog,qog$year==2010)
qog100$cntry<-countrycode(sjmisc::to_label(qog100$ccodecow), "cown","country.name.en")
qog100 <- qog100 %>% dplyr::select(cntry,year,ht_regtype1) %>%
  as.data.frame()
qog100$regtype<-sjmisc::to_label(qog100$ht_regtype1)
qog100$year <- NULL
qog100$ht_regtype1 <- NULL
table(qog100$regtype)


ethnic10<-as.numeric(rowMeans(data_wide17[,2:12])) # mean over last 10 years (2000 - 2010)
physint10<-as.numeric(rowMeans(data_wide20[,2:12])) # mean over last 10 years (2000 - 2010)
terror10<-as.numeric(rowMeans(data_wide21[,2:12])) # mean over last 10 years (2000 - 2010)
legit<-as.numeric(data_wide18[,2]) # mean over last 10 years (2000 - 2010)
legit2<-as.numeric(data_wide19[,2]) # mean over last 10 years (2000 - 2010)
qogthingy<-data.frame(data_wide19[,1],ethnic10,legit,legit2,physint10,terror10)
colnames(qogthingy)[1]<-c("cntry")
qogthingy<-merge(x=qogthingy, y=qog100, by="cntry")

#qogthingy<-data.frame(data_wide17[,1],ethnic10)
#colnames(qogthingy)[1]<-c("cntry")
#combined <- merge(x=qogthingy, y=combined, by="cntry")


qog_cs <-read_spss("C:/Users/Favone/Downloads/qog_std_cs_jan17.sav") #loading dataset
qog_cs$cntry<-countrycode(qog_cs$ccodecow, "cown","country.name.en")
qog_cs$legit3<-as.numeric(qog_cs$gov_ixlegitimacyindex)

legit_dat<-data.frame(qog_cs$cntry,qog_cs$legit3)
colnames(legit_dat)<-c("cntry","legit3")

aggrdelib <- merge(x=legit_dat, y=aggrdelib, by="cntry")

aggrdelib$legit3 <- range01(aggrdelib$legit3)
cor(na.omit(aggrdelib[,c(4,5,7,13)]))
cor(na.omit(aggrdelib[,c(4,5,2,6:13)]))

aggrdelib$legit <- 1-range01(aggrdelib$legit)
aggrdelib$legit2 <- 1-range01(aggrdelib$legit2)
aggrdelib$asia <- aggrdelib$e.asia + aggrdelib$s.e.asia + aggrdelib$s.asia + aggrdelib$pacific

SFI <-read_spss("C:/Users/Favone/Downloads/SFIv2016.sav") #loading dataset
SFI <- subset(SFI,SFI$year==2015)
SFI$cntry <-countrycode(SFI$country, "country.name.en","country.name.en") 
SFI$cntry[79] <- "North Korea"
SFI$cntry <-countrycode(SFI$cntry, "country.name.en","country.name.en") 
SFI$legitimacy <- SFI$legit
SFI$legit <- NULL


aggrdelib <- merge(x=SFI, y=aggrdelib, by="cntry")

hdi <- read_csv("hdi.csv")
hdi$cntry<-countrycode(hdi$Country, "country.name.en","country.name.en")
hdi$'1990'<- NULL ; hdi$'1991'<- NULL ; hdi$'1992'<- NULL ; hdi$'1993'<- NULL
hdi$'1994'<- NULL; hdi$'1995'<- NULL; hdi$'1996'<- NULL; hdi$'1997'<- NULL
hdi$'1998'<- NULL ; hdi$'1999'<- NULL ; hdi$'2011'<- NULL ; hdi$'2012'<- NULL
hdi$'2013'<- NULL; hdi$'2014'<- NULL; hdi$'2015'<- NULL;hdi$`HDI Rank (2015)`<- NULL
hdi$Country<- NULL
hdi10<-as.numeric(rowMeans(hdi[,1:11])) 
hdat<-data.frame(hdi$cntry,hdi10) 
names(hdat)<-c("cntry","hdi10")

aggrdelib <- merge(x=hdat, y=aggrdelib, by="cntry")
#table(aggrdelib$hdi10)
#gc()
#combined <- merge(x=hdat, y=combined, by="cntry")


#colnames(aggrdelib)[9]<-"e_p_polity"
#lop<-subset(vdems_start,vdems_start$year==2010)

cor(na.omit(aggrdelib2[,2:17]))
cor(na.omit(data.frame(aggrdelib$polity10,aggrdelib$delib10)))

#hist(vdems$v2dlconslt)
#hist(vdems$v2xcl_disc)
#table(vdems_start$e_boix_regime,vdems_start$year)
#table(vdems_sub$e_p_polity,vdems_sub$year)

##### merging time ####

combined <- merge(x=merged, y=aggrdelib, by="cntry")
table(combined$cntry)

combined <- as.data.frame(combined)
combined$gov_trust <- as.numeric(combined$gov_trust)
combined$age <- as.numeric(combined$age)
combined$income <- as.numeric(combined$income)
combined$educ <- as.numeric(combined$educ)
combined$sex <- as.factor(combined$sex)
combined$authoritarian <- as.numeric(combined$authoritarian)
combined$safety <- as.numeric(combined$safety)
combined$demtoday <- as.numeric(combined$demtoday)
combined$latino <- factor(combined$latino)
combined$afro <- factor(combined$afro)
combined$americas <- factor(combined$americas)
combined$asia <- combined$e.asia + combined$s.e.asia + combined$s.asia + combined$pacific

cor(na.omit(data.frame(combined$gov_trust,combined$income,combined$educ, #Socioeconomic factors
                       combined$delib10, combined$polity10, combined$gdp10, combined$demtoday)))

combined$cntry<-as.factor(combined$cntry)


#combined$polity10 <- combined$polity10*20-10

#combined$regime <- combined$polity10
#combined$regime[combined$polity_autodummy==1] <- "auto"
#combined$regime[combined$polity_anodummy==1] <- "ano"
#combined$regime[combined$polity_demdummy==1] <- "demo"

hist(combined$gov_trust)
qplot(combined$gov_trust)

combined$cntry



#physi_s <- vdems_sub2 %>% dplyr::select(cntryears, franz)

#combined <- merge(combined, physi_s, by = "cntryears")

#physi2_s <- ddply(combined,~cntry,
#                summarise,politcat=mean(franz,na.rm=T))

#combined <- merge(combined, physi2_s, by = "cntry")


vdems_sub2$cntryears <- paste(vdems_sub2$cntry,vdems_sub2$year)

physi <- vdems_sub2 %>% dplyr::select(cntryears, perc)

combined <- merge(combined, physi, by = "cntryears")

physi2 <- ddply(combined,~cntry,
                     summarise,physviol=mean(perc,na.rm=T))

combined <- merge(combined, physi2, by = "cntry")





#physi_22$dis3 <-round(physi_22$dis2)

#unique(physi2_s$cntry)

#1-2.5
#3-5
#5.5 - 7
#physi2_s$politcat2<-8-((physi2_s$politcat) * (7/10))
#11 

#physi2_s$polity_demdummy <- physi2_s$politcat2
#physi2_s$polity_demdummy [physi2_s$politcat2 <= 2.5] <- 1
#physi2_s$polity_demdummy [physi2_s$politcat2 >  2.5] <- 0
#table(physi2_s$polity_demdummy)

#physi2_s$polity_anodummy <- physi2_s$politcat2
#physi2_s$polity_anodummy[physi2_s$politcat2 > 2.5 & physi2_s$politcat2 < 5.5] <- 1
#physi2_s$polity_anodummy[physi2_s$politcat2 <= 2.5 | physi2_s$politcat2 >= 5.5] <- 0
#table(physi2_s$polity_anodummy)

#physi2_s$polity_autodummy <- physi2_s$politcat2
#physi2_s$polity_autodummy[physi2_s$politcat2 >= 5.5] <- 1
#physi2_s$polity_autodummy[physi2_s$politcat2 < 5.5] <- 0
#table(physi2_s$polity_autodummy)

#physi2_s$regime <- physi2_s$politcat
#physi2_s$regime[physi2_s$polity_autodummy==1] <- "auto"
#physi2_s$regime[physi2_s$polity_anodummy==1] <- "ano"
#physi2_s$regime[physi2_s$polity_demdummy==1] <- "demo"

#unique(physi2_s$cntry)

table(physi2_s$regime)

qog2000 <- subset(qog,qog$year==2010 |
                       qog$year==2011 |  
                       qog$year==2012 | 
                       qog$year==2013 | 
                       qog$year==2014 | 
                       qog$year==2015)

table(qog2000$gd_ptsa,qog2000$year)

qog2000$perc2 <- qog2000$gd_ptsa

qog2000$cntry<-countrycode(qog2000$ccodecow, "cown","country.name.en")

qog2000$cntryears <- paste(qog2000$cntry,qog2000$year)

physi2000 <- qog2000 %>% dplyr::select(cntryears, perc2)

combined <- merge(combined, physi2000, by = "cntryears")

physi22000 <- ddply(combined,~cntry,
                summarise,terror=mean(perc2,na.rm=T))

combined <- merge(combined, physi22000, by = "cntry")

#table(qog2000$undp_hdi,qog2000$year)

#physiff <- qog2000 %>% dplyr::select(cntryears, undp_hdi)

#combined <- merge(combined, physiff, by = "cntryears")

#physiff2 <- ddply(combined,~cntry,
#                    summarise,hdi=mean(undp_hdi,na.rm=T))

#combined <- merge(combined, physiff2, by = "cntry")


gni <- read_csv("GNI.csv",  skip = 1)

gni$`2015`<-gsub("ttt","",gni$`2015`)
gni$`2015`<-gsub("ff","",gni$`2015`)
gni$`2015`<-gsub("sss","",gni$`2015`)
gni$`2015`<-gsub("uuu","",gni$`2015`)
gni$`2015`<-gsub("o","",gni$`2015`)
gni$`2015` <- as.numeric(gni$`2015`)

gni$`2014`<-gsub("ttt","",gni$`2014`)
gni$`2014`<-gsub("ff","",gni$`2014`)
gni$`2014`<-gsub("sss","",gni$`2014`)
gni$`2014`<-gsub("uuu","",gni$`2014`)
gni$`2014`<-gsub("o","",gni$`2014`)
gni$`2014` <- as.numeric(gni$`2014`)

gni<-gni[,c(2,23:28)]

gni<-gather(as.data.frame(gni),key = "Country")
names(gni) <- c("cntry","year","gni")

gni$cntry<-countrycode(gni$cntry,"country.name.en","country.name.en")
gni$cntryears <- paste(gni$cntry,gni$year)

physigni <- gni %>% dplyr::select(cntryears, gni)

combined <- merge(combined, physigni, by = "cntryears")

physigni2 <- ddply(combined,~cntry,
                    summarise,gni_c=mean(gni,na.rm=T))

combined <- merge(combined, physigni2, by = "cntry")



```



















